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What drives the liking of video advertisements? The authors analyzed neural signals during ad exposure from three functional magnetic resonance imaging (fMRI) data sets (113 participants from two countries watching 85 video ads) with automated meta-analytic decoding (Neurosynth). These brain-based measures of psychological processes—including perception and language (information processing), executive function and memory (cognitive functions), and social cognition and emotion (social-affective response)—predicted subsequent self-report ad liking, with emotion and memory being the earliest predictors after the first three seconds. Over the span of ad exposure, while the predictiveness of emotion peaked early and fell, that of social cognition had a peak-and-stable pattern, followed by a late peak of predictiveness in perception and executive function. At the aggregate level, neural signals—especially those associated with social-affective response—improved the prediction of out-of-sample ad liking compared with traditional anatomically based neuroimaging analysis and self-report liking. Finally, early-onset social-affective response predicted population ad liking in a behavioral replication. Overall, this study helps delineate the psychological mechanisms underlying ad processing and ad liking and proposes a novel neuroscience-based approach for generating psychological insights and improving out-of-sample predictions. [ABSTRACT FROM AUTHOR]

As social media influencer endorsement gains significance in marketing communication, an increasing number of influencers have started to incorporate product information into their sponsored content. This study examines the effectiveness of product attribute description in the context of sponsored videos. Using a field dataset of 598 sponsored videos, we demonstrate that influencers' use of product attribute description as an endorsement strategy has a negative impact on video engagement, and this effect is stronger for trial versus awareness campaigns. However, the negative impact is reversed to a positive one when product attribute description is employed for utilitarian products but not for hedonic products. These results reveal that the effectiveness of product attribute description depends on the nature of the product and the campaign objectives. Overall, this study contributes to the understanding of influencer marketing effectiveness and sheds light on the nuances of endorsement strategies. Practical implications on how to optimise endorsement effectiveness and video performance are discussed. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess the immediate effect of video feedback on the regulation and control of the standing back tuck somersault in the absence of vision. Two groups of male parkour athletes performed the standing back tuck somersault under both open and closed eyes conditions. The first group received video feedback, while the second group received verbal feedback. Concurrent analysis, including kinetic data from a force plate (Kistler Quattro-Jump) and kinematic data in two-dimensional by Kinovea freeware, was employed for motion and technical performance analysis. The results indicate that the loss of vision during the standing back tuck somersault affected only the take-off and ungrouping angle, as well as the vertical velocity and displacement. These effects were consistent regardless of the type of feedback provided (i.e., video feedback or verbal feedback). Furthermore, a significant Vision × Feedback interaction was observed at the level of technical performance. This suggests that the use of video feedback enabled the parkour athletes to maintain a high level of technical performance both with and without vision (i.e., 13.56 vs. 13.00 points, respectively, p >.05 and d = 2.233). However, the verbal feedback group technical performance declined significantly under the no-vision condition compared with the vision condition (13.14 vs. 10.25 points, respectively, with and without vision, p <.001 and d = 2.382). We concluded that when the movement is proprioceptively controlled (i.e., without vision), the video feedback enables the athletes to globally assess the technical deficiencies arising from the lack of vision and to correct them. These findings are discussed based on parkour athletes' ability to evaluate the kinematic parameters of the movement. [ABSTRACT FROM AUTHOR]

Although in-stream video advertising is common, its effects on advertisement (ad) information encoding remain unclear. We investigated the effects of in-stream video advertising by comparing two groups: those watching mid-roll (between the program) ads and those watching pre- and post-roll (before and after the program, respectively) ads. To elucidate how advertising content is encoded in the context of in-stream video advertising, we integrated two theoretical frameworks: the negative emotion–memory model (NEMM) and the limited capacity model of motivated–mediated message processing (LC4MP). We used electroencephalography (EEG) to assess negative emotions and bottom-up attention during advertisement viewing. The findings indicate that the first mid-roll ad induced negative emotions, but these feelings were attenuated during subsequent mid-rolls. In addition, negative emotions induced by mid-roll ads attenuated the role of bottom-up attention in the information encoding process. However, the pre- and post-roll ads were not accompanied by negative emotions; thus, bottom-up attention played a major role in the information encoding of these ads. The results also suggest that despite the negative emotions experienced during mid-rolls, such transient negative reactions did not affect purchase intention for the advertised products. [ABSTRACT FROM AUTHOR]

This research examines when and how the speed of video ads influences consumers' perceptions of luxuriousness and their subsequent behaviors toward products or brands featured in the ads. Across 12 experiments (total N = 27,227, five preregistered), the authors demonstrate that when a video ad depicts a product in slow motion (vs. regular speed), consumers perceive the featured product or brand as more luxurious. The effect emerges across various product categories (chocolate, shampoo, mineral water, wine) and in different countries (United States, United Kingdom, France). Tests of mediation and moderation suggest that the effect occurs because viewing a slow-motion ad increases feelings of immersion, which lead consumers to expect greater hedonic value from the featured product and thus view it as more luxurious. Consistent with this account, the effect weakens when video blurriness or buffering impairs the immersive viewing experience afforded by slow motion, and the effect attenuates among consumers who are very weakly or very strongly predisposed to experience immersion. Finally, by enhancing perceptions of luxuriousness, slow motion subsequently boosts consumers' desire for the featured product or brand (as manifested by higher willingness to pay, purchase intentions, and ad click rates), particularly when the goal to consume luxury is salient (vs. not). [ABSTRACT FROM AUTHOR]

This paper describes the SocialVidStim-a database of video stimuli available to the scientific community depicting positive and negative social evaluative and neutral statements. The SocialVidStim comprises 53 diverse individuals reflecting the demographic makeup of the USA, ranging from 9 to 41 years old, saying 20-60 positive and 20-60 negative social evaluative statements (e.g. 'You are a very trustworthy/annoying person'), and 20-60 neutral statements (e.g. 'The sky is blue'), totaling 5793 videos post-production. The SocialVidStim are designed for use in behavioral and functional magetic resonance imaging paradigms, across developmental stages, in diverse populations. This study describes stimuli development and reports initial validity and reliability data on a subset videos (N = 1890) depicting individuals aged 18-41 years. Raters perceive videos as expected: positive videos elicit positively valenced ratings, negative videos elicit negatively valenced ratings and neutral videos are rated as neutral. Test-retest reliability data demonstrate intraclass correlations in the good-to-excellent range for negative and positive videos and the moderate range for neutral videos. We also report small effects on valence and arousal that should be considered during stimuli selection, including match between rater and actor sex and actor believability. The SocialVidStim is a resource for researchers and we offer suggestions for using the SocialVidStim in future research.

Few-shot action recognition aims to learn a model that can be easily adapted to identify novel action classifications using only a few labeled samples. Recent methods primarily focus on visual features and fail to fully utilize the available classification title of the video. In addition, they capture higher-order temporal relationships among video frames through averaging, which neglects the long-range dependencies information of the video. To address these issues, we designed a novel cross-modal guided spatio-temporal enrichment network (X-STEN) for few-shot action recognition. The model includes a cross-modal spatial enrichment module (X-SEM), a temporal enrichment module (TEM), and a non-parametric metrics module (NMM). Firstly, we extract and fuse multi-modal feature representations of videos. Then, we enhance the spatial context information of the video using the X-SEM and model the temporal context information of the video using the TEM. Finally, we generate the query and support prototypes and measure the similarity between them. Extensive experiments demonstrate that our X-STEN achieve excellent results on few-shot splits of Kinetics, HMDB51 and UCF101. Importantly, our method outperforms prior work on Kinetics by a wide margin (13.9%). [ABSTRACT FROM AUTHOR]

Electrophoretic displays (EPDs) are popular in consumer electronics, like e‐readers and tags, for their paper‐like visuals and minimal power consumption. However, EPDs struggle to rapidly switch to target grayscales due to the inherent limitations in the response speed of their display particles. Video halftoning technology achieves a smoother video grayscale display, but it also inevitably reduces the image quality and affects the viewing experience. Therefore, this paper proposes an EPD driving method based on image semantic segmentation. The method could distinguish the dynamic region and the static region accurately. For static areas, high‐definition driving waveforms were used to ensure the image display accuracy. For the dynamic area, dynamic target and dynamic text were processed by different algorithms, respectively, and the speed‐driving waveforms were used to maintain the fluency and clarity of the video display. With the method we proposed, the display quality for EPD in complex video playback was optimized significantly. Compared to DBS refresh techniques, the SSIM had increased from 4% to 82% and the PSNR had increased from 2.56 dB to 10.89 dB with our method, significantly enhancing the video playback performance of EPDs. [ABSTRACT FROM AUTHOR]

Video presentation has become ubiquitous in paradigms investigating the neural and behavioral responses to observed actions. In spite of the great interest in uncovering the processing of observed bodily movements and actions in neuroscience and cognitive science, at present, no standardized set of video stimuli for action observation research in neuroimaging settings exists. To facilitate future action observation research, we developed an open-access database of 135 high-definition videos of a male actor performing object-oriented actions. Actions from 3 categories: kinematically natural and goal-intact (Normal), kinematically unnatural and goal-intact (How), or kinematically natural and goal-violating (What), directed toward 15 different objects were filmed from 3 angles. Psychometric evaluation of the database revealed high video recognition accuracy (Mean accuracy = 88.61 %) and substantial inter-rater agreement (Fleiss' Kappa = 0.702), establishing excellent validity and reliability. Videos' exact timing of motion onset was identified using a custom motion detection frame-differencing procedure. Based on its outcome, the videos were edited to assure that motion begins at the second frame of each video. The videos' timing of category recognition was also identified using a novel behavioral up-down staircase procedure. The identified timings can be incorporated in future experimental designs to counteract jittered stimulus onsets, thus vastly improving the sensitivity of neuroimaging experiments. All videos, their psychometric evaluations, and the timing of their frame of category recognition, as well as our custom programs for performing these evaluations on our, or on other similar video databases, are available at the Open Science Framework (https://osf.io/zexc4/). [ABSTRACT FROM AUTHOR]

This study focused on the perception of the students on the video tutorials as a learning tool used in teaching Bread and Pastry production and its relationship with their baking performance. The descriptive-correlational research approach has been employed that uses video tutorials and survey questionnaires. The respondents are Grade 10 students in San Antonio National High School at San Antonio, Quezon during the school year 2023-2024. In identifying the respondents of the study, total enumeration of the population was considered, thus, no sampling technique was applied. The respondents generally perceived the video tutorials positively with favorable responses on objectives, content, instructions, audio and visual quality, and frequency of use. Similarly, their baking performances received positive feedback, particularly in presentation, taste and texture, time management, and food safety and hygiene. The study found out that the video tutorials in terms of its components such as objective, content, and instructions do not significantly related to students baking technical skills performance. Moreover, in significant relationship also exerted between students' baking performance and the video tutorial audio quality, visual quality and frequency of use. On the other hand, the students' average baking performance was associated with video tutorials visual quality and frequency of use. [ABSTRACT FROM AUTHOR]

Since the evolution of technology, it created a growing importance in the education sector. The use of video graphics or multimedia tools becomes a useful instrument in learning and teaching in the education industry. This study determined the correlation between the utilization of video graphics in teaching Hairdressing and the students' performance. A descriptive method of research was applied to accomplish this study. A total of thirty-three (33) Grade Eleven TVL students served as the respondents of this study. The researcher used a survey questionnaire in checklist format as the main research instrument to gather needed data. Statistics tools such as mean, standard deviation mean scores, and frequencies were used. The significance was tested at the 0.05 probability level. Based on the data collected, the perception of the students to the utilization of video graphics in teaching Hairdressing has an overall interpretation of "highly observed". It was revealed that there is a significant difference between the pre and post-test exams, considering the delivery of the lesson, teacher's mastery, efficiency, and feedback. The results indicate no significant relationship except for efficiency and performing hair treatment, audio, and post-service activities. The results also showed a positive learning development in terms of lesson adaptation through pre- and post-test scores and performance scores based on what the students knew before and after exposure to the video graphics. [ABSTRACT FROM AUTHOR]

This study investigated the effect of using the Accelerative Integrated Method (AIM), with or without additional revision and video-guided practice, on junior primary students' spoken recall accuracy in French. The same beginner AIM French activities were taught by a language specialist to 12 classes in four junior primary levels. Three different conditions of instruction were used each week: (1) one session of AIM instruction, (2) one session of AIM instruction plus one session of revision, and (3) one session of AIM instruction, plus one session of revision, and three sessions of video input. The instruction and revision sessions were facilitated by a language teacher, whereas the video sessions were played by the main class teacher. A two-way ANOVA revealed that Year 1 students benefited from a revision session with the language teacher, but Prep and Year 1 did not benefit from further video exposure. Conversely, the Years 2 and 3 students did not benefit from extra revision with the language teacher, but they did benefit from additional video exposure. Direct observation of the student response to the AIM suggested that the pace of the program needed to be slower for the younger learners. [ABSTRACT FROM AUTHOR]

Identifying reinforcers is crucial for many evidence‐based behavior change programs. Videos may be a type of reinforcer worth considering given the accessibility of small electronic devices and the rates at which children watch and enjoy screen time. We used the Multiple‐Stimulus‐Without‐Replacement Preference Assessment Tool (MSWO PAT) to identify high‐ and low‐preferred videos for three children diagnosed with autism spectrum disorder (ASD). We then evaluated the predictive validity of the MSWO PAT by arranging a concurrent‐operants reinforcer assessment for puzzle and sorting tasks. The results show that the high‐preferred video maintained higher levels of responding for puzzle and sorting tasks for two of the three children. Implications for using videos as reinforcers for children with ASD, limitations, and areas for future research are also discussed. [ABSTRACT FROM AUTHOR]

Teacher contingency, in which teachers adapt their support to the learner's level of understanding is assumed to be the distinguishing characteristic of scaffolding. Yet, classroom studies on the topic scarcely exist. The current study examined the process of contingency in classroom settings. Three novice and three experienced English language teachers were video‐recorded to compile a 9‐h corpus. The model of contingent teaching was used to code the different stages of scaffolding. Conversation analysis was applied to provide an emic perspective of each stage. The results indicated different contingent and non‐contingent teaching patterns for novice and experienced teachers. The non‐contingent teaching of novice teachers was characterized by skipping the diagnosis stage, failing to adapt the support to the learners' level of understanding by using high‐support moves, and failing to check learners' understanding. The experienced teachers mainly diagnosed their learners' understanding, checked their diagnosis, and used a wider array of strategies to provide interventions. Their non‐contingent teaching stemmed from maladjustment of support to their learners' level of understanding. The implications for theory and practice are then discussed. [ABSTRACT FROM AUTHOR]

The effect of emotional valence in word retrieval is well‐established in native languages, but findings in a foreign language remain inconsistent. This study investigated the impact of emotional valence on free word recall in English as a foreign language after video exposure. Participants were upper‐intermediate English speakers with Spanish as their mother tongue who watched eight emotionally valenced and four neutral videos, followed by an immediate free recall task and a delayed recall task after 2 weeks. Generalized linear mixed models showed that word valence influenced recall, with neutral words being less easily recalled than negative words, and a decline in recall between sessions. These findings support the Motivated Attention Account, which suggests that motivationally significant stimuli capture more attention than neutral stimuli, regardless of their polarity. Furthermore, the results align with previous evidence in native language research and some studies in foreign language contexts. This study highlights the robust effect of emotional valence on word recall at different time intervals, using authentic input with upper‐intermediate English speakers who have Spanish as their mother tongue and who learned English later in life. [ABSTRACT FROM AUTHOR]

In recent years, there has been a proliferation of weakly supervised methods in the field of video anomaly detection. Despite significant progress in existing research, these efforts have primarily focused on addressing this issue within Euclidean space. Conducting weakly supervised video anomaly detection in Euclidean space imposes a fundamental limitation by constraining the ability to model complex patterns due to the dimensionality constraints of the embedding space and lacking the capacity to model long-term contextual information. This inadequacy can lead to misjudgments of anomalous events due to insufficient video representation. However, hyperbolic space has shown significant potential for modeling complex data, offering new insights. In this paper, we rethink weakly supervised video anomaly detection with a novel perspective: transforming video features from Euclidean space into hyperbolic space may enable the network to learn implicit relationships in normal and anomalous videos, thereby enhancing its ability to effectively distinguish between them. Finally, to validate our approach, we conducted extensive experiments on the UCF-Crime and XD-Violence datasets. Experimental results show that our method not only has the lowest number of parameters but also achieves state-of-the-art performance on the XD-Violence dataset using only RGB information. [ABSTRACT FROM AUTHOR]

With the mainstreaming of virtual reality and 360° video, media consumption will increasingly include engaging with highly immersive messages alongside more traditional media formats. That means users will need to potentially switch back and forth between highly immersive and relatively non-immersive media messages. However, little is yet known about how transitioning between drastically different immersion levels may affect the cognitive and emotional processing of those messages. This paper presents the first study to examine the nature of excitation transfer effects during transitions across media technologies of different immersive quality. We find support for the existence of transfer of arousal elicited by an initial immersive message (T1 stimulus) to the arousal response elicited by a subsequent non-immersive media experience (T2 stimulus). However, physiological arousal at T2 did not linearly increase with the level of message immersion at T1. Additionally, intra-stimulus patterns suggest excitation escalation and decay during T1 could influence inherent arousal response to the T2 stimulus, highlighting the importance of an analytic strategy that allows for an examination of how excitation transfer dynamically unfolds. Implications and future research directions related to the psychological processing of sequences of messages, particularly those of varied levels of technological immersion, are discussed. [ABSTRACT FROM AUTHOR]

Conventionally, the goal of Video Instance Segmentation (VIS) is to segment and categorize objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos. To address this limitation, we make the following three contributions. First, we introduce the novel task of Open-Vocabulary Video Instance Segmentation (OV-VIS), which aims to simultaneously segment, track, and classify objects in videos from open-set categories, including novel categories unseen during training. Second, to benchmark OV-VIS, we collect a Large-Vocabulary Video Instance Segmentation dataset (LV-VIS), that contains well-annotated objects from 1196 diverse categories, significantly surpassing the category size of existing datasets by more than an order of magnitude. Third, we propose a transformer-based OV-VIS model, OV2Seg+, which associates per-frame segmentation masks with a memory-induced transformer and clarifies objects in videos with a voting module given language guidance. In addition, to monitor the progress, we set up the evaluation protocols for OV-VIS and propose a set of strong baseline models to facilitate future endeavors. Extensive experiments on LV-VIS and four existing VIS datasets demonstrate the strong zero-shot generalization ability of OV2Seg+. The dataset and code are released here https://github.com/haochenheheda/LVVIS. The competition website is provided here https://www.codabench.org/competitions/1748. [ABSTRACT FROM AUTHOR]

With the rapid development of depth sensor, more and more RGB-D videos could be obtained. Identifying the foreground in RGB-D videos is a fundamental and important task. However, the existing salient object detection (SOD) works only focus on either static RGB-D images or RGB videos, ignoring the collaborating of RGB-D and video information. In this paper, we first collect a new annotated RGB-D video SOD (ViDSOD-100) dataset, which contains 100 videos within a total of 9362 frames, acquired from diverse natural scenes. All the frames in each video are manually annotated to a high-quality saliency annotation. Moreover, we propose a new baseline model, named attentive triple-fusion network (ATF-Net), for RGB-D video salient object detection. Our method aggregates the appearance information from an input RGB image, spatio-temporal information from an estimated motion map, and the geometry information from the depth map by devising three modality-specific branches and a multi-modality integration branch. The modality-specific branches extract the representation of different inputs, while the multi-modality integration branch combines the multi-level modality-specific features by introducing the encoder feature aggregation (MEA) modules and decoder feature aggregation (MDA) modules. The experimental findings conducted on both our newly introduced ViDSOD-100 dataset and the well-established DAVSOD dataset highlight the superior performance of the proposed ATF-Net.This performance enhancement is demonstrated both quantitatively and qualitatively, surpassing the capabilities of current state-of-the-art techniques across various domains, including RGB-D saliency detection, video saliency detection, and video object segmentation. We shall release our data, our results, and our code upon the publication of this work. [ABSTRACT FROM AUTHOR]

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This study investigates how affect is manifested in the pragmeme of delivering a health directive in the Covid-19 context and how affect co-participates in the pragmeme. This research draws on theoretical insights from pragmeme theory, relevance theory, and the recent distributed language view, with the aim of contributing to the development of a language-body-environment perspective on pragmatic acts. By way of illustration, two videos used in BBC reports were analyzed, one featuring the UK Prime Minister Boris Johnson's order to the British people to stay at home and the other being an unofficial, self-made video that also appeals to the public to remain at home. This paper argues that affect in its various manifestations embodies affective potential, which should be regarded as being part and parcel of the pragmeme. [ABSTRACT FROM AUTHOR]

Macro-expression spotting is an important prior step in many dynamic facial expression analysis applications. It automatically detects the onset and offset image frames of a macro-expression in the video. The state-of-the-art methods of macro-expression spotting characterize the movement of facial muscle through explicit analysis of the optical flow map and have achieved promising results. However, optical flow map estimation and expression spotting in these methods are performed in two separate and successive stages. In this paper, we propose a new dual-branch network to achieve unified optimization for expression spotting and optical flow estimation tasks. The proposed dual-branch network implicitly learns optical flow during training and enriches the feature representation with motion information. During inference, we use only the encoder of the optical flow estimation network for motion feature extraction and integrate it with expression spotting into a one-stage framework. The proposed method eliminates the need to construct optical flow maps explicitly during inference and significantly reduces the computational cost. We also apply a consistency constraint on the global- and local-level semantic features of the clip to guide the model to focus on the category-consistent regions of the video. We evaluate the proposed methods extensively on two popular facial expression spotting datasets, CAS(ME) 2 and SAMM Long Videos. The experimental results show that compared with the state-of-the-art methods, the proposed method improves the F1-scores for MaE spotting by 5.81 % and 1.57 % on the CAS(ME) 2 and SAMM Long Videos datasets respectively. [ABSTRACT FROM AUTHOR]

This research paper addresses an important challenge in book digitization, i.e., accurately predicting frames where text visibility is optimal. Existing models often suffer from high computational complexity, resulting in inefficiencies in automation and accuracy. In contrast, our proposed models offer a solution with lower complexity and higher accuracy. Leveraging a diverse dataset of book flipping videos, we introduce three novel models: the Regular CNN LeNet-5 Model, the Custom LSTM Model, and the 3D CNN Model. Evaluation reveals that our 3D CNN Model achieves an accuracy score of 99.01%, with 377,921 parameters. These models demonstrate a significant increase in efficiency in terms of accuracy metric with significantly less number of parametrers. Thereby the proposed approach enhances the process of identifying frames of interest. Our findings highlight the transformative potential of these models in streamlining book digitization workflows and improving accessibility to digitized textual content. This study contributes valuable insights at the intersection of computer vision, machine learning, and digitization efforts, offering a promising avenue for enhancing the usability of digitized textual resources. [ABSTRACT FROM AUTHOR]

Video anomaly detection is always formulated as frame prediction task which only learned on normal data and detects deviations as anomalies. However, previous methods lack sufficient spatiotemporal constraints on moving objects, making it difficult to learn compact normal distributions and anomalies near the boundary will be misclassified as normal. Besides, the inadequate exploration of diverse normal patterns results in mode missing and unlearned normal patterns will be misclassified as anomalies. To address these problems, we propose an object-level Diverse Motion-conditioned Adversarial Predictive Network for video anomaly detection which combines conditional variational generation with adversarial learning to mitigate false detection. We design a motion-guided generator that controls the generation process conditioned on optical flows to accurately memorize spatiotemporal correlations of normal data. We employ the diversity regularization strategy which explicitly preserves the recurrent structure of normal data in continuous latent space to ensure full utilization of diverse patterns. Additionally, we combine an input clip with the object it generates to synthesize an anomaly near the boundary, then employ a video discriminator to perceive subtle differences between normal and abnormal data, making them more distinguishable. Extensive experiments conducted on public datasets illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the fuzziness of anomaly definition and the complexity of scenes in real video data, video anomaly detection is still a challenging task. In this work, we explored a novel lightweight dual branch convolution neural architecture that can separate appearance-motion representations to capture spatial and temporal information, respectively, since abnormal events are usually different from normal cases in appearance or motion behavior. Considering the channel redundancy problem in the traditional neural network, and the feature information processed by different branches is different, the corresponding channel attenuation is carried out, which greatly improves the speed of anomaly detection while maintaining the performance of the model. In order to improve the utilization of key features, we exploited a Channel Squeeze and Excitation module and insert it into the encoder part of the network to focus on the channel correlation and adaptively recalibrate the characteristic response of the channel. The importance of each feature channel is automatically acquired through learning, and then according to the importance, the useful channels are promoted and the channels that are not useful for the current task are suppressed. In addition, in order to increase the reconstruction error of the motion encoder and consider the diversity of the normal patterns, we propose to use a memory module to augment the motion U-Net, where the items in the memory record the prototype mode of the normal data. The experiments on three benchmark datasets, UCSD Ped2, CHUK Avenue, and ShanghaiTech, demonstrate that our method achieves AUC scores of 96.3%, 87.4%, and 73.5%, respectively. The experimental speed reaches 55fps, showing a competitive performance relative to the current state of research. [ABSTRACT FROM AUTHOR]

Objective To evaluate the information quality of hypertension-related videos on the TikTok short video platform and assess their potential value for public health education. Methods In June 2024, we searched the TikTok short video platform using the keyword "hypertension" selecting the top 200 videos and ultimately including 177 videos for quality evaluation. Structured content integrity assessment tools and the DISCERN video quality assessment tool were used to evaluate the videos. The correlation between video quality and characteristics (duration, likes, comments, favorites, and shares) was studied. Results The 177 videos received 11 250 432 likes, 366 588 comments, 3 265 929 favorites, and 3 780 513 shares. The median video length was 111 seconds (74, 158), and the median upload days were 672 days (147, 878). 89.3% of the videos were posted by doctors, 8.5% by individual science communicators, and 2.3% by news agencies. 98.9% of the videos had subtitles, all featured individuals, and only 26.0% included animated demonstrations. Content integrity scores were: definition 0.19±0.40, symptoms 0.11±0.32, risk factors 0.29±0.50, assessment 0.36±0.45, management 0.81±0.47, and outcomes 0.63±0.58. 90.4% of the videos had a DISCERN score of ≤50, indicating "average" or below quality, with no significant difference in DISCERN scores among different publishers (P>0.05). DISCERN scores were significantly correlated with video duration (r=0.409, P<0.001). Conclusion The overall quality of hypertension-related videos on TikTok is low, with a significant positive correlation between video duration and quality. High-quality videos receive less attention, making TikTok an unsuitable platform for hypertension education due to low information quality and reliability. [ABSTRACT FROM AUTHOR]

Real-time quality monitoring through molten pool images is a critical focus in researching high-quality, intelligent automated welding. However, challenges such as the dynamic nature of the molten pool, changes in camera perspective, and variations in pool shape make defect detection using single-frame images difficult. We propose a multi-scale fusion method for defect monitoring based on molten pool videos to address these issues. This method analyzes the temporal changes in light spots on the molten pool surface, transferring features between frames to capture dynamic behavior. Our approach employs multi-scale feature fusion using row and column convolutions along with a gated fusion module to accommodate variations in pool size and position, enabling the detection of light spot changes of different sizes and directions from coarse to fine. Additionally, incorporating mixed attention with row and column features enables the model to capture the characteristics of the molten pool more efficiently. Our method achieves an accuracy of 97.416% on a molten pool video dataset, with a processing time of 16 ms per sample. Experimental results on the UCF101-24 and JHMDB datasets also demonstrate the method's generalization capability. [ABSTRACT FROM AUTHOR]

Temporal action localization plays a significant role in video understanding, which aims to recognize action category as well as temporal interval in untrimmed videos. Most of previous transformer-based methods employ a feature space of single-temporal granularity. However, low-level temporal features can not provide enough semantic information for action recognition while high-level temporal features lack rich details for boundary localization. To address the above issue, we propose a multi-granularity transformer fusion framework (MGTF) to localize temporal actions in videos. Specifically, the MGTF builds a multi-granularity feature fusion pipeline based on transformer, and uses a direct set prediction strategy to generate action instances. Through top-down cross-granularity attention interaction, the low-level features of boundary details and high-level semantic information can be combined to improve the feature discrimination. To reduce computation cost, we design temporal shift attention to adaptively focus on a sparse set of key segments. In addition, actionness regression head is utilized to refine the confidence score of different candidate instances. As a self-contained system, MGTF achieves state-of-the-art performance on THUMOS'14 and comparable performance on ActivityNet-1.3. Ablation studies and qualitative visualization also demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Animation of hand‐drawn sketches is an adorable art. It allows the animator to generate animations with expressive freedom and requires significant expertise. In this work, we introduce a novel sketch animation framework designed to address inherent challenges, such as motion extraction, motion transfer, and occlusion. The framework takes an exemplar video input featuring a moving object and utilizes a robust motion transfer technique to animate the input sketch. We show comparative evaluations that demonstrate the superior performance of our method over existing sketch animation techniques. Notably, our approach exhibits a higher level of user accessibility in contrast to conventional sketch‐based animation systems, positioning it as a promising contributor to the field of sketch animation. https://graphics-research-group.github.io/SketchAnim/ [ABSTRACT FROM AUTHOR]

This study analyzed the content of videos on Al Jazeera and Al Hadath platforms regarding the Sudanese war that started on April 15, 2023. The aim was to identify the news coverage objectives of both platforms by examining their Tik Tok videos and assessing the media frameworks used to cover the Sudanese war. The researcher employed the content analysis method and monitored information using the form. The analysis - covered videos published on the two TikTok platforms from March 1 to June 30, 2024, totaling 189 videos – (87) were from Al Jazeera and (102) from Al Hadath . The study found that the primary framework of the videos was "conflict," focusing on reporting battles and military clashes. The study also revealed that the videos aimed to provide information, offer explanations and interpretations, and convey an image of the events, with providing information being the predominant goal. [ABSTRACT FROM AUTHOR]

Aim: Today, Youtube, which is also used as a source of health information, is one of the most visited social platforms. The aim of this study was to evaluate the content and reliability of videos on undescended testicular operations for surgeons and parents. Materials and Methods: On 25/9/2023, the keywords "undescended testicle operation," "orchiopexy," and "children" were searched on the YouTube search engine. The selected videos were divided into two groups: useful and misleading. In addition to the descriptive characteristics of the videos, the reliability, quality and content of the videos were analysed using the global quality score (GQS), modified DISCERN (mDISCERN), and video power index (VPI) score. Results: Of the 62 videos evaluated in our study, 69.4% (n=43) were of high quality, 14.5% (n=9) were of medium quality and 16.1% (n=4) were of low quality. GQS and mDISCERN scores were higher in the useful group (p=0.002, p=0.001, respectively). While 91.1% (n=51) of the videos in the useful group were addressed to healthcare professionals (p=0.024), 78.6% (n=44) of these videos were uploaded by physicians. A positive correlation was observed between GQS and mDISCERN scores (p<0.001). Conclusion: Videos uploaded by academicians, surgeons and addressed to health professionals were found to be reliable and of high quality. Undescended testicular operations videos uploaded to social platforms should be prepared by a committee with health professionals and checked for compliance with scientific standards. [ABSTRACT FROM AUTHOR]

Cheap amateur videodiscs are a common household object in Buyi (Bouyei) villages in Guizhou, southwest China. Buyi videographers are hired to record and edit footage of local life-cycle rituals and household banquets, and copied videodiscs are distributed informally to, and repeatedly viewed by, relatives, neighbours, and fellow villagers—mostly middle-aged women. Attentive to on-demand, vernacular video products as such that are underexplored in media anthropology, I examine the Buyi domestic videos as straddling the private and public realms of cultural production. While playing a significant role in the construction of 'home' as mediated spaces and bodily practices, these domestic videos thrive on an intimate public, which emerges out of an on-demand economy based on kinship-based and place-based networks. This video sociality involves a milieu of relationality for those that enter into its sphere, binding shared objects, aesthetics, emotions, and experiences together. [ABSTRACT FROM AUTHOR]

This article proposes a weakly supervised multi-feature fusion pedestrian re-identification method, which introduces a multi-feature fusion mechanism to extract feature information from different layers into the same feature space and fuse them into the deep and shallow joint features. The goal is to fully utilize the rich information in the image and improve the performance and robustness of the pedestrian re-identification model. Secondly, by matching the target character with unprocessed surveillance videos, one only needs to know that the identity of a person appears in the video, without annotating the identity of a person in any of the frames of the video during the training process. This simplifies the annotation of training images by replacing accurate annotations with broad annotations; that is, it puts the pedestrian identities that appeared in the video in one package and assigns a video-level label to each package. This greatly reduces the annotation work and transforms this weakly supervised pedestrian re-identification challenge into a multi-instance and multi-label learning problem. The experimental results show that the method proposed in this paper is effective and can significantly improve mAP. [ABSTRACT FROM AUTHOR]

With the growing diverse student population in U.S. public schools, an increased focus on cultural-related matters has surfaced in education. As a result, consultation training must prepare novice consultants with the knowledge and skills needed to provide multicultural consultation. Technology can be used to develop novice consultants' multicultural consultation skills. This conceptual paper presents four strategies that can be used with technological applications to prepare novice consultants to deliver multicultural consultation services. Specific attention to how graduate-level training can infuse technology into existing educational practices, such as structured supervision, cross-university dialogs, video simulations, and virtual-based service learning to strengthen multicultural consultation skill development. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the multi-target tracking (MOT) task in satellite videos. To achieve efficient and accurate tracking, we propose a transformer-distillation-based end-to-end joint detection and tracking (JDT) method. Specifically, (1) considering that targets in satellite videos usually have small scales and are shot from a bird's-eye view, we propose a pixel-wise transformer-based feature distillation module through which useful object representations are learned via pixel-wise distillation using a strong teacher detection network; (2) targets in satellite videos, such as airplanes, ships, and vehicles, usually have similar appearances, so we propose a temperature-controllable key feature learning objective function, and by highlighting the learning of similar features during distilling, the tracking accuracy for such objects can be further improved; (3) we propose a method that is based on an end-to-end network but simultaneously learns from a highly precise teacher network and tracking head during training so that the tracking accuracy of the end-to-end network can be improved via distillation without compromising efficiency. The experimental results on three recently released publicly available datasets demonstrated the superior performance of the proposed method for satellite videos. The proposed method achieved over 90% overall tracking performance on the AIR-MOT dataset. [ABSTRACT FROM AUTHOR]

High-quality video object segmentation is a challenging visual computing task. Interactive segmentation can improve segmentation results. This paper proposes a multi-round interactive dynamic propagation instance-level video object segmentation network based on click interaction. The network consists of two parts: a user interaction segmentation module and a bidirectional dynamic propagation module. A prior segmentation network was designed in the user interaction segmentation module to better segment objects of different scales that users click on. The dynamic propagation network achieves high-precision video object segmentation through the bidirectional propagation and fusion of segmentation masks obtained from multiple rounds of interaction. Experiments on interactive segmentation datasets and video object segmentation datasets show that our method achieves state-of-the-art segmentation results with fewer click interactions. [ABSTRACT FROM AUTHOR]

Thermal videos provide a privacy-preserving yet information-rich data source for remote health monitoring, especially for respiration rate (RR) estimation. This paper introduces an end-to-end deep learning approach to RR measurement using thermal video data. A detection transformer (DeTr) first finds the subject's facial region of interest in each thermal frame. A respiratory signal is estimated from a dynamically cropped thermal video using 3D convolutional neural networks and bi-directional long short-term memory stages. To account for the expected phase shift between the respiration measured using a respiratory effort belt vs. a facial video, a novel loss function based on negative maximum cross-correlation and absolute frequency peak difference was introduced. Thermal recordings from 22 subjects, with simultaneous gold standard respiratory effort measurements, were studied while sitting or standing, both with and without a face mask. The RR estimation results showed that our proposed method outperformed existing models, achieving an error of only 1.6 breaths per minute across the four conditions. The proposed method sets a new State-of-the-Art for RR estimation accuracy, while still permitting real-time RR estimation. [ABSTRACT FROM AUTHOR]

Collaboration among road agents, such as connected autonomous vehicles and roadside units, enhances driving performance by enabling the exchange of valuable information. However, existing collaboration methods predominantly focus on perception tasks and rely on single-frame static information sharing, which limits the effective exchange of temporal data and hinders broader applications of collaboration. To address this challenge, we propose CoPnP, a novel collaborative joint perception and prediction system, whose core innovation is to realize multi-frame spatial–temporal information sharing. To achieve effective and communication-efficient information sharing, two novel designs are proposed: (1) a task-oriented spatial–temporal information-refinement model, which filters redundant and noisy multi-frame features into concise representations; (2) a spatial–temporal importance-aware feature-fusion model, which comprehensively fuses features from various agents. The proposed CoPnP expands the benefits of collaboration among road agents to the joint perception and prediction task. The experimental results demonstrate that CoPnP outperforms existing state-of-the-art collaboration methods, achieving a significant performance-communication trade-off and yielding up to 11.51%/10.34% Intersection over union and 12.31%/10.96% video panoptic quality gains over single-agent PnP on the OPV2V/V2XSet datasets. [ABSTRACT FROM AUTHOR]

Hyperspectral video provides rich spatial and spectral information, which is crucial for object tracking in complex scenarios. Despite extensive research, existing methods often face an inherent trade-off between rich spectral information and redundant noisy information. This dilemma arises from the efficient utilization of hyperspectral image data channels. To alleviate this problem, this paper introduces a hierarchical spectral attention network for hyperspectral object tracking. We employ a spectral band attention mechanism with adaptive soft threshold to examine the correlations across spectral bands, which integrates the information available in various spectral bands and eliminates redundant information. Moreover, we integrate spectral attention into a hierarchical tracking network to improve the integration of spectral and spatial information. The experimental results on entire public hyperspectral competition dataset WHISPER2020 show the superior performance of our proposed method compared with that of several related methods in visual effects and objective evaluation. [ABSTRACT FROM AUTHOR]

Increases in display resolution, frame rate, and bit depth, particularly with advances in stereoscopic 3D (S3D) displays, have increased demand for efficient compression throughout the imaging pipeline. To meet such requirements, typically the aim is to reduce bandwidth while presenting content that is visually indistinguishable from the original uncompressed versions. Subjective image quality assessment is essential and multiple methods have been proposed. Of these, the ISO/IEC 29170‐2 flicker paradigm is a rigorous method used to define visually lossless performance. However, it is possible that the enhanced sensitivity to artifacts in the presence of flicker does not predict visibility under natural viewing conditions. Here, we test this prediction using high‐dynamic range S3D images and video under flicker and non‐flicker protocols. As hypothesized, sensitivity to artifacts was greater when using the flicker paradigm, but no differences were observed between the non‐flicker paradigms. Results were modeled using the Pyramid of Visibility, which predicted artifact detection driven by moderately low spatial frequencies. Overall, our results confirm the flicker paradigm is a conservative estimate of visually lossless behavior; it is highly unlikely to miss artifacts that would be visible under normal viewing. Conversely, artifacts identified by the flicker protocol may not be problematic in practice. [ABSTRACT FROM AUTHOR]

Temporal inconsistency is the annoying artifact that has been commonly introduced in low-light video enhancement, but current methods tend to overlook the significance of utilizing both data-centric clues and model-centric design to tackle this problem. In this context, our work makes a comprehensive exploration from the following three aspects. First, to enrich the scene diversity and motion flexibility, we construct a synthetic diverse low/normal-light paired video dataset with a carefully designed low-light simulation strategy, which can effectively complement existing real captured datasets. Second, for better temporal dependency utilization, we develop a Temporally Consistent Enhancer Network (TCE-Net) that consists of stacked 3D convolutions and 2D convolutions to exploit spatial-temporal clues in videos. Last, the temporal dynamic feature dependencies are exploited to obtain consistency constraints for different frame indexes. All these efforts are powered by a Spatial-Temporal Compatible Learning (STCL) optimization technique, which dynamically constructs specific training loss functions adaptively on different datasets. As such, multiple-frame information can be effectively utilized and different levels of information from the network can be feasibly integrated, thus expanding the synergies on different kinds of data and offering visually better results in terms of illumination distribution, color consistency, texture details, and temporal coherence. Extensive experimental results on various real-world low-light video datasets clearly demonstrate the proposed method achieves superior performance to state-of-the-art methods. Our code and synthesized low-light video database will be publicly available at https://github.com/lingyzhu0101/low-light-video-enhancement.git. [ABSTRACT FROM AUTHOR]

To validate the Manipal Bedside Swallowing Screening Test (MBSST) for assessing aspiration in individuals with tracheostomy using Video Fluoroscopic Swallow Study (VFSS). A total of 53 tracheostomized participants were screened for aspiration at bedside and VFSS across different consistencies. Penetration Aspiration Scale (PAS) was used to substantiate the severity of aspiration if present. The test demonstrated both high sensitivity (91%; 95% confidence interval = 70.8–98.9) and diagnostic accuracy (96%; 95% confidence interval = 87.0-99.5) for detecting aspiration in individuals with tracheostomy. The Manipal Bedside Swallowing Screening Test is an easy to administer tool that has sufficient sensitivity to detect aspiration in individuals with tracheostomy. The MBSST will be an easy to administer tool and will help in screening for aspiration at bedside without any instrumental examination. [ABSTRACT FROM AUTHOR]

Both visual and haptic softness perception have recently been shown to have multiple dimensions, such as deformability, granularity, fluidity, surface softness, and roughness. During haptic exploration, people adjust their hand motions (exploratory procedures, EPs) based on the material qualities of the object and the particular information they intend to acquire. Some of these EPs are also shown to be associated with perceived softness dimensions, for example, stroking a silk blouse or applying pressure to a pillow. Here, we aimed to investigate whether we can manipulate observers' judgments about softness attributes through exposure to videos of others performing various EPs on everyday soft materials. In two experiments, participants watched two videos of the same material: one with a corresponding EP and the other without correspondence; then, they judged these materials based on 12 softness-related adjectives (semantic differentiation method). The results of the second experiment suggested that when the EP is congruent with the dimension from which the material is chosen, the ratings for the adjectives from the same dimension are higher than the incongruent EP. This study provides evidence that participants can assess material properties from optic and mechanical cues without needing haptic signals. Additionally, our findings indicate that manipulating the hand motion can selectively facilitate material-related judgments. [ABSTRACT FROM AUTHOR]

This qualitative study examines the use of the Video Timeline-Anchored Comment (VTC) tool in asynchronous online courses to explore students' discussion during video-based lectures. Applying this recently available tool of human-computer interaction (HCI) for asynchronous online video discussions enables students to watch video-based lectures while timely commenting on the content, and peers' thoughts, or asking questions. Thirty students from two graduate-level adult education courses at a US institution participated in the study. The findings demonstrate that the VTC tool helps engage online students in asynchronous video-based lecture discussions and increases their interactions with the content, peers, and their instructor. Our research suggests a model that encourages interactive engagement to enhance learners' cognitive presence. This could help bridge the gap between online lectures and offline social networks in an asynchronous learning setting by facilitating real-time connections. We conclude by integrating the HCI tool with the interactive discussion model, students' online interactions during video lecture discussions can be improved and sustained. [ABSTRACT FROM AUTHOR]

Previous studies have investigated which context-sharing interfaces are effective in improving task performance; however, consistent results have yet to be found. In this study, we developed a convenient remote collaboration system that provides multiple context-sharing interfaces in a single platform (2D video, 360° video, and 3D reconstruction view). All interfaces can reflect live updates of environmental changes, and we aimed to clarify the effect of the interface on the quality of remote collaboration. Thirty participants were recruited to perform a target-finding-and-placing scenario. Using both objective and subjective metrics, we compared the task performance among the three interface conditions. The results showed that participants completed the task faster and reported a better collaborative experience in the 3D interface condition. Moreover, we found a strong preference for the 3D view interface. These results suggest that providing 3D reconstructed spatial information can enable remote experts to instruct local workers more effectively. [ABSTRACT FROM AUTHOR]

Given the wealth of research available on what components to include in a syllabus and how that affects instructor perception, there is a lack of research on how to present the syllabus to engage students in learning about the course. Our study examined the implementation of two multimedia syllabi developed for large lecture math courses: a graphic syllabus and a video version. After reviewing their respective syllabi, students were given a syllabus content quiz and course perception survey. Findings indicate no significant difference between students' syllabus quiz performance and overall survey responses between groups. Students responded favorably to the survey regardless of syllabus type. Last, there was no observed relationship between the length of each syllabus segment video and the frequency at which students watched each segment. Implications of this study will address engagement with the syllabus and provide additional considerations for syllabus development. [ABSTRACT FROM AUTHOR]

This article presents a study that examines the effectiveness of prerecorded video lectures compared to traditional in-person lectures. The study focuses on a college physics course and surveys students who experienced both types of lectures in the same semester. The survey data reveals that students generally found the video lectures to be effective and felt that they learned the subject material. However, they expressed a preference for in-person lectures and disagreed with the idea of having all lectures delivered solely through video. The results suggest that while video lectures are convenient, students still value the benefits of in-person lectures, such as immediate interaction and the ability to ask questions. Future research will explore ways to enhance student engagement with video lectures, such as offering virtual office hours. Overall, the findings indicate that while students are open to learning from video lectures, they still prefer in-person lectures. [Extracted from the article]