Search

Conducting a reasonable assessment of whether the durability and bearing capacity of concrete used in existing bridge pile foundations can meet all requirements is an important prerequisite for ensuring the reuse of existing bridge pile foundations. With this in mind, this study evaluates the project of the Beijing–Taibei Highway in Shandong Province that must be restored and enlarged. Using static load and concrete durability testing methods, the bearing capacity characteristics and durability of seven existing bridge pile foundations were investigated. Using attribute identification theory, a systematic technique for determining the reuse value of existing piles is proposed. The test examination findings demonstrate that the change curve corresponding to the pile foundation's ultimate bearing capacity matches the concrete compressive strength curve. The pile foundations had lower ultimate bearing capacity than average. The concrete compressive strengths of the three test piles were 18%, 16.3%, and 17.5% lower than the average compressive strengths of the seven test piles. A comprehensive durability evaluation method for assessing existing piles can effectively evaluate the reuse value of existing piles and promote sustainable highway reconstruction and expansion projects. [ABSTRACT FROM AUTHOR]

Clothing attribute recognition has become an essential technology, which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes. However, existing methods cannot recognize newly added attributes and may fail to capture region-level visual features. To address the aforementioned issues, a region-aware fashion contrastive language-image pre-training (RaF-CLIP) model was proposed. This model aligned cropped and segmented images with category and multiple fine-grained attribute texts, achieving the matching of fashion region and corresponding texts through contrastive learning. Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes, and to further improve the accuracy of retrieval, an attribute-guided composed network (AGCN) as an additional component on RaF-CLIP was introduced, specifically designed for composed image retrieval. This task aimed to modify the reference image based on textual expressions to retrieve the expected target. By adopting a transformer-based bidirectional attention and gating mechanism, it realized the fusion and selection of image features and attribute text features. Experimental results show that the proposed model achieves a mean precision of 0.663 3 for attribute recognition tasks and a recall@10 (recall@ k is defined as the percentage of correct samples appearing in the top k retrieval results) of 39.18 for composed image retrieval task, satisfying user needs for freely searching for clothing through images and texts. [ABSTRACT FROM AUTHOR]

Scene classification has established itself as a challenging research problem. Compared to images of individual objects, scene images could be much more semantically complex and abstract. Their difference mainly lies in the level of granularity of recognition. Yet, image recognition serves as a key pillar for the good performance of scene recognition as the knowledge attained from object images can be used for accurate recognition of scenes. The existing scene recognition methods only take the category label of the scene into consideration. However, we find that the contextual information that contains detailed local descriptions are also beneficial in allowing the scene recognition model to be more discriminative. In this paper, we aim to improve scene recognition using attribute and category label information encoded in objects. Based on the complementarity of attribute and category labels, we propose a Multi-task Attribute-Scene Recognition (MASR) network which learns a category embedding and at the same time predicts scene attributes. Attribute acquisition and object annotation are tedious and time consuming tasks. We tackle the problem by proposing a partially supervised annotation strategy in which human intervention is significantly reduced. The strategy provides a much more cost-effective solution to real world scenarios, and requires considerably less annotation efforts. Moreover, we re-weight the attribute predictions considering the level of importance indicated by the object detected scores. Using the proposed method, we efficiently annotate attribute labels for four large-scale datasets, and systematically investigate how scene and attribute recognition benefit from each other. The experimental results demonstrate that MASR learns a more discriminative representation and achieves competitive recognition performance compared to the state-of-the-art methods.

Numerous deep perception technologies and methods are built on the foundation of pedestrian feature identification. It covers various fields, including autonomous driving, spying, and object tracking. A recent study area is the identification of personality traits that has attracted much interest in video surveillance. Identifying a person’s distinct areas is complex and plays an incredibly significant role. This paper presents a current method applied to networks of primary convolutional neurons to locate the area connected to the Person attribute. Using Individual Feature Identification, the features of a person, such as gender, age, fashion sense, and equipment, have received much attention in video surveillance analytics. This Article adopted a Conv-Attentional image transformer that broke down the most discriminating Attribute and region into multiple grades. The feed-forward system and conv-attention are the components of serial blocks, and parallel blocks have two attention-focused tactics: direct cross-layer attention and feature interpolation. It also provides a flexible Attribute Localization Module (ALM) to learn the regional aspects of each Attribute are considered at several levels, and the most discriminating areas are selected adaptively. We draw the conclusion that hybrid transformers outperform pure transformers in this instance. The extensive experimental results indicate that the proposed hybrid technique achieves higher results than the current strategies on four unique private characteristic datasets, i.e., RapV2, RapV1, PETA, and PA100K.

Multi-Camera Multi-Target Tracking (MTMCT) has challenges such as viewpoint and pose variations, scale and illumination changes, and occlusion. Available MTMCT approaches have high computational complexity and are not sufficiently robust in the mentioned challenges. In this work, an Attribute Recognition-based MTMCT(AR-MTMCT) framework is presented for real-time application. This framework performs object detection, re-Identification (Re-Id) feature extraction, and attribute recognition in an end-to-end manner. Applying attributes highly improves MTMC online tracking performance in the mentioned challenges. The pipeline of AR-MTMCT consists of three modules. The first module is a novel one-shot Single-Camera Tracking (SCT) architecture named Attribute Recognition-Multi Object Tracking (AR-MOT) which performs object detection, Re-Id feature extraction, and attributes recognition using one backbone through multi-task learning. Hierarchical clustering is performed in the second module to deal with the detection of several instances of one identity in the overlapping areas of cameras. In the last module, a new data association algorithm is performed using spatial information to reduce matching candidates. We also have proposed an efficient strategy in the data association algorithm to remove lost tracks by making a trade-off between the number of lost tracks and the maximum lost time. Evaluation and training of AR-MTMCT have been done on the large-scale MTA dataset. The proposed system has been improved by 20% and 11%, respectively, compared to the WDA method in IDF1 and IDs metrics. Also, the AR-MTMCT outperforms the state-of-the-art methods by a large margin on decreasing computational complexities. [ABSTRACT FROM AUTHOR]

Person re-identification (Re-ID) can determine whether a pedestrian target can be matched across diverse regions or cameras, thereby alleviating the problem between massive surveillance data and inefficient manual retrieval. Inspired by attribute-person recognition (APR) network, this paper proposes an improved Re-ID method based on attribute learning, which uses an attribute-guided attention mechanism module and an attribute dependency learning module to learn fine-grained attribute features and rich dependencies among them. After that, a joint model with the integration of attribute recognition and person identity recognition is built for end-to-end training. Experimental results show that the proposed method can effectively improve Re-ID accuracy and achieve a competitive recognition performance. [ABSTRACT FROM AUTHOR]

In video surveillance, visual person attributes such as gender, backpack, type of clothing are crucial for person search or re-identification. For detecting and retrieving these attributes with high accuracy, the availability of high-quality videos is a necessity in general. However, this cannot be guaranteed in general surveillance videos or images; beside improving hardware technology, improving inference algorithms on low-resolution data is valuable. This paper attempts to propose two solutions in this direction: designing a combined neural network architecture from existing architectures, and a novel combination approach toward re-identification on low-resolution videos. The proposed architecture introduces a combined Neural Network architecture, called SRMAR, that jointly trains Super Resolution and Multi Attribute Recognition models for more effective recognition. Experiments on two benchmark datasets demonstrate the effectiveness and applicability of the proposed neural network architecture for low-resolution multi-attribute recognition. Furthermore, a higher-level linear combination scheme that optimally combines the proposed SRMAR architecture and multi-attribute recognition network is presented, yielding superior results in low-resolution person attribute recognition.

The giant panda (Ailuropoda melanoleuca) has long attracted the attention of conservationists as a flagship and umbrella species. Collecting attribute information on the age structure and sex ratio of the wild giant panda populations can support our understanding of their status and the design of more effective conservation schemes. In view of the shortcomings of traditional methods, which cannot automatically recognize the age and sex of giant pandas, we designed a SENet (Squeeze-and-Excitation Network)-based model to automatically recognize the attributes of giant pandas from their vocalizations. We focused on the recognition of age groups (juvenile and adult) and sex of giant pandas. The reason for using vocalizations is that among the modes of animal communication, sound has the advantages of long transmission distances, strong penetrating power, and rich information. We collected a dataset of calls from 28 captive giant panda individuals, with a total duration of 1298.02 s of recordings. We used MFCC (Mel-frequency Cepstral Coefficients), which is an acoustic feature, as inputs for the SENet. Considering that small datasets are not conducive to convergence in the training process, we increased the size of the training data via SpecAugment. In addition, we used focal loss to reduce the impact of data imbalance. Our results showed that the F1 scores of our method for recognizing age group and sex reached 96.46% ± 5.71% and 85.85% ± 7.99%, respectively, demonstrating that the automatic recognition of giant panda attributes based on their vocalizations is feasible and effective. This more convenient, quick, timesaving, and laborsaving attribute recognition method can be used in the investigation of wild giant pandas in the future. [ABSTRACT FROM AUTHOR]

The areas along the mountainous gas pipelines are complicated in geographic and geomorphic conditions, and thus greatly affected by earthquakes. Therefore, the safety and reliability assessment methods of mountainous gas pipelines under the influence of earthquakes were analyzed, which is of important strategic significance to ensure the safety of natural gas supply. From the prospective of the seismic risk and pipeline vulnerability factors, the method of calculating the weight of the index system under group decision-making with the improved Borda method was put forward. Meanwhile, the calculation methods of multi-index comprehensive measurement mentioned in the attribute recognition theory were divided into the types with "or" and "and" logics. Then, the calculation method of multi-index comprehensive measurement with "and" logics was proposed, and the assessment system for the seismic vulnerability of mountainous gas pipelines was established. In addition, the assessment system was applied to a mountainous gas pipeline in western Sichuan, and by introducing the attribute recognition theory, the attribute recognition was performed with the basic data combined with the expert opinions. In this way, the vulnerability level of the pipeline section was obtained. Generally, the results could provide reference for the prediction of the vulnerable pipeline sections in earthquakes and the development of the appropriate measures.

Most of the person re-identification (re-ID) algorithms based on deep learning mainly learn the global feature representation of pedestrians, while ignoring the important role of fine-grained pedestrian attribute features on re-ID tasks. Pedestrian attributes are middle-level semantic features, which have invariance in different poses, camera views, and illumination conditions. Considering the robustness and promotion of pedestrian attributes for person re-ID task, we propose an Attribute-guided Global and Part-level identity Network (AGPNet), which consists of a global identity task, a part-level identity task, and a pedestrian attributes learning task. AGPNet takes advantage of perceived semantic information of pedestrian attributes and deploys them as guidance to attend to human body regions and learn robust feature representation in the feature representation construction stage. Extensive experiments on two large-scale person re-ID datasets (Market-1501 and DukeMTMC-reID) show the effectiveness of our method, which is competitive with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

How and where to learn the discriminative feature has always been a critical issue of person re-identification (re-ID). Most of the previous methods focus on extracting global representation from overall identity image and lack of high-level semantic attribute information. Based on the considerations above, we focus on looking for a way to make people's attribute play a role in person re-identification task. In this paper, a novel multitask-like network, namely, Attribute-Identity Complementary Network (AICNet), is designed. It contains two branches to learn the identity and attribute feature separately, and a reciprocal interaction process is added to enrich the discrimination of the resulting feature. In addition, in order to better extract high-level semantic attribute information, multi-scale features are combined in the process of attribute extraction. With the help of this attribute-identity complementary strategy, the generated feature can be guided to learn most distinctive attribute feature that is most relevant to identity feature. The present paper carries out extensive experiments on two large-scale datasets, including Market-1501, and DukeMTMC-reID, after which it is found that our attribute-identity complementary framework significantly outperforms the baseline model and achieves competitive performance compared with the state-of-the-art person re-ID methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vehicle Re-Identification (V-ReID) is a critical task that associates the same vehicle across images from different camera viewpoints. Many works explore attribute clues to enhance V-ReID; however, there is usually a lack of effective interaction between the attribute-related modules and final V-ReID objective. In this work, we propose a new method to efficiently explore discriminative information from vehicle attributes (for instance, color and type). We introduce AttributeNet (ANet) that jointly extracts identity-relevant features and attribute features. We enable the interaction by distilling the ReID-helpful attribute feature and adding it into the general ReID feature to increase the discrimination power. Moreover, we propose a constraint, named Amelioration Constraint (AC), which encourages the feature after adding attribute features onto the general ReID feature to be more discriminative than the original general ReID feature. We validate the effectiveness of our framework on three challenging datasets. Experimental results show that our method achieves the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The human brain has been a main source of inspiration for designing deep learning models. Recently, inspired by the specialized functions of two cerebral hemispheres in processing low and high spatial frequency information, some dual-path neural networks with global and local branches have been proposed to deal with both coarse- and fine-grained visual tasks simultaneously. However, in existing works, the interhemispheric communication mechanism, which is responded by the corpus callosum, the largest white matter structure in the human brain that connecting the left and right cerebral hemispheres, is still not fully explored and exploited. This paper aims to explore how the corpus callosum can inspire us to enable transfer and integration of information between global and local branches in hemisphere-inspired artificial neural networks, such that one branch can leverage the other's learned knowledge and benefit each other. To this end, we propose a gated intercommunication unit to selectively transfer useful knowledge between the two branches via attention mechanisms to alleviate the negative transfer. Experiments on sb-MNIST and two pedestrian attribute datasets show that the proposed method outperforms the compared ones in most cases.

Surveillance cameras are everywhere keeping an eye on pedestrians or people as they navigate through the scene. Within this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails the extraction of different attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem with a host of challenges even for human observers. As such, the topic has rightly attracted attention recently. In this work, we integrate trainable Gabor wavelet (TGW) layers inside a convolution neural network (CNN). Whereas other researchers have used fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We test our method on publicly available challenging datasets and demonstrate considerable improvements over state of the art approaches.

Recent advancement of research in biometrics, computer vision, and natural language processing has discovered opportunities for person retrieval from surveillance videos using textual query. The prime objective of a surveillance system is to locate a person using a description, e.g., a short woman with a pink t-shirt and white skirt carrying a black purse. She has brown hair. Such a description contains attributes like gender, height, type of clothing, colour of clothing, hair colour, and accessories. Such attributes are formally known as soft biometrics. They help bridge the semantic gap between a human description and a machine as a textual query contains the person's soft biometric attributes. It is also not feasible to manually search through huge volumes of surveillance footage to retrieve a specific person. Hence, automatic person retrieval using vision and language-based algorithms is becoming popular. In comparison to other state-of-the-art reviews, the contribution of the paper is as follows: 1. Recommends most discriminative soft biometrics for specific challenging conditions. 2. Integrates benchmark datasets and retrieval methods for objective performance evaluation. 3. A complete snapshot of techniques based on features, classifiers, number of soft biometric attributes, type of the deep neural networks, and performance measures. 4. The comprehensive coverage of person retrieval from handcrafted features based methods to end-to-end approaches based on natural language description. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Person attribute recognition, i.e., the prediction of a fixed set of semantic attributes given an image of a person, becomes an important topic in the field of computer vision. Recently, methods based on convolutional neural networks have shown outstanding performance in this area. They usually employ a CNN network to mine the shared feature representation followed by several layers for attribute classification. To improve the representation ability of the model, many methods element-add or concatenate coarse and fine feature maps to fuse information at different feature levels. However, these methods didn't fully exploit the interaction of multi-level convolutional feature maps for person attribute analysis and not consider the correlation of attributes for the same person. In this paper, we introduce a kind of correlation feature, which exploits the high order interaction of coarse and fine feature maps to capture the robust feature representation from multi-level convolution layers as the image representation for person attribute recognition. Moreover, we propose an intraperson attribute loss to explicitly model the correlation of attributes for the same person. We experiment our proposed model on CIFAR-10 dataset, Berkeley Human Attributes dataset, PA-100 K dataset, and experimental results show the better performance of the feature representation and the effectiveness of intra-person attribute loss. [ABSTRACT FROM AUTHOR]

Person attribute recognition aims to identify the attribute labels from the pedestrian images. Extracting contextual relation from the images and attributes, including the spatial-semantic relations, the spatial context and the semantic correlation, is beneficial to enhance the discrimination of the features for recognizing the attributes. Thus, this work proposes a sequence contextual relation learning (SCRL) method to capture these relations. It first embeds the images and attributes into sequences in two branches. Then SCRL flexibly learns the contextual relation from the sequences with the parallel attention model structure, which integrates the inter-attention and intra-attention models. The inter-attention module is utilized to extract the spatial-semantic relations, while the intra-attention is designed to gain the spatial context and the semantic correlation. Both attention modules are comprised of several parallel attention units and each unit can obtain the pairwise relations in one subspace. Therefore, they obtain the relations in multiple subspaces, which can improve the comprehensiveness of the relation learning. Additionally, for the sake of better extraction of spatial-semantic relations, this paper employs connectionist temporal classification (CTC) loss which is capable of driving the network to enforce monotonic alignment between the image and attribute. It can also accelerate the convergence of the network by the algorithm in it. Extensive experiments on five public datasets, i.e., Market-1501 attribute, Duke attribute, PETA, RAP and PA-100K datasets, demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Crowd attribute recognition is a challenging task for crowd video understanding because a crowd video often contains multiple attributes from various types. Traditional deep learning-based methods directly treat this recognition problem as a multiple binary classification problem and represent the video by vectorizing and fusing the separately learned spatial and temporal features in the fully connected layers. Therefore, the correlations between these attributes may not be well captured. In this paper, a bidirectional recurrent prediction model with a semantic-aware attention mechanism is proposed to explore the spatio-temporal and semantic relations between the attributes for more accurate recognition. The ConvLSTM is introduced for feature representation to capture the spatio-temporal structure of the crowd videos and facilitate the visual attention. The bidirectional recurrent attention module is proposed for sequential attribute prediction by associating each subcategory attributes to corresponding semantic-related regions iteratively. The experiments and evaluations on the challenging WWW crowd video dataset not only show that our approach significantly outperforms the state-of-the-art methods but also verify that our approach can effectively capture the spatio-temporal and semantic relations of the crowd attributes. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Person re-identification (re-ID) has been gaining in popularity in the research community owing to its numerous applications and growing importance in the surveillance industry. Recent methods often employ partial features for person re-ID and offer fine-grained information beneficial for person retrieval. In this paper, we focus on learning improved partial discriminative features using a deep convolutional neural architecture, which includes a pyramid spatial pooling module for efficient person feature representation. Furthermore, we propose a multi-task convolutional network that learns both personal attributes and identities in an end-to-end framework. Our approach incorporates partial features and global features for identity and attribute prediction, respectively. Experiments on several large-scale person re-ID benchmark data sets demonstrate the accuracy of our approach. For example, we report rank-1 accuracies of 85.37% (+3.47 %) and 92.81% (+0.51 %) on the DukeMTMC re-ID and Market-1501 data sets, respectively. The proposed method shows encouraging improvements compared with the state-of-the-art methods.

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• We annotate attribute labels on two large-scale person re-identification datasets. • We propose APR to improve re-ID by exploiting global and detailed information. • We introduce a module to leverage the correlation between attributes. • We speed-up the retrieval of re-ID by ten times with only a 2.92% accuracy drop. • We achieve competitive re-ID performance with the state-of-the-art methods. Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID methods only take identity labels of pedestrians into consideration. However, we find the attributes, containing detailed local descriptions, are beneficial in allowing the re-ID model to learn more discriminative feature representations. In this paper, based on the complementarity of attribute labels and ID labels, we propose an attribute-person recognition (APR) network, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes. We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other. In addition, we re-weight the attribute predictions considering the dependencies and correlations among the attributes. The experimental results on two large-scale re-ID benchmarks demonstrate that by learning a more discriminative representation, APR achieves competitive re-ID performance compared with the state-of-the-art methods. We use APR to speed up the retrieval process by ten times with a minor accuracy drop of 2.92% on Market-1501. Besides, we also apply APR on the attribute recognition task and demonstrate improvement over the baselines. [ABSTRACT FROM AUTHOR]

This study proposes a deep neural network model based on an encoder-decoder structure for visual dialogs. Ongoing linguistic understanding of the dialog history and context is important to generate correct answers to questions in visual dialogs followed by questions and answers regarding images. Nevertheless, in many cases, a visual understanding that can identify scenes or object attributes contained in images is beneficial. Hence, in the proposed model, by employing a separate person detector and an attribute recognizer in addition to visual features extracted from the entire input image at the encoding stage using a convolutional neural network, we emphasize attributes, such as gender, age, and dress concept of the people in the corresponding image and use them to generate answers. The results of the experiments conducted using VisDial v0.9, a large benchmark dataset, confirmed that the proposed model performed well. [ABSTRACT FROM AUTHOR]

Lou, H., 2019. Massive ship fault data retrieval algorithm supporting complex query in cloud computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1013–1018. Coconut Creek (Florida), ISSN 0749-0208. Aiming at the problems of the current retrieval algorithm for massive ship fault data retrieval in cloud computing, which does not support complex query, large retrieval cost and low precision, a massive ship fault data retrieval algorithm supporting complex query in cloud computing based on query performance prediction is proposed. Introducing the Lagrangian algorithm to preprocess the ship fault data information; combining the attribute values of the ship fault data and the context to calculate the similarity between different attributes, and identifying the same attribute according to the similarity; The attribute recognition result is queried by using multiple retrieval models, and the model complex query request is converted into a one-dimensional query key value, and the query key values obtained by the plurality of models are predicted, and the search result with the optimal prediction performance of the complex query mode is used as the final result. The experimental results show that the proposed algorithm has good retrieval accuracy in complex query mode, and it has less overhead than the current search algorithm. [ABSTRACT FROM AUTHOR]

This study considered heated metal mark attribute recognition based on compressed convolutional neural networks (CNNs) models. Based on our previous works, the heated metal mark image benchmark dataset was further expanded. State-of-the-art lightweight CNNs models were selected. Technologies of pruning, compressing, weight quantization were introduced and analyzed. Then, a multi-label model training method was devised. Moreover, the proposed models were deployed on Android devices. Finally, comprehensive experiments were evaluated. The results show that, with the fine-tuned compressed CNNs model, the recognition rate of attributes meta type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity were 0.803, 0.837, 0.825, 0.812, 0.883, 0.817 and 0.894, respectively. The best model obtained an overall performance of 0.823. Comparing with traditional CNNs, the adopted compressed multi-label model greatly improved the training efficiency and reduced the space occupation, with a relatively small decrease in recognition accuracy. The running time on Android devices was acceptable. It is shown that the proposed model is applicable for real time application and is convenient to implement on mobile or embedded devices scenarios. [ABSTRACT FROM AUTHOR]

Abstract When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine its appearance given your experience, nothing more. Similarly, AI solutions can try to hallucinate missing information with specific deep learning architectures, suitably trained with people with and without occlusions. The goal of this work is to generate a complete image of a person, given an occluded version in input, that should be a) without occlusion b) similar at pixel level to a completely visible people shape c) capable to conserve similar visual attributes (e.g. male/female) of the original one. For the purpose, we propose a new approach by integrating the state-of-the-art of neural network architectures, namely U-nets and GANs, as well as discriminative attribute classification nets, with an architecture specifically designed to de-occlude people shapes. The network is trained to optimize a Loss function which could take into account the aforementioned objectives. As well we propose two datasets for testing our solution: the first one, occluded RAP, created automatically by occluding real shapes of the RAP dataset created by Li et al. (2016) (which collects also attributes of the people aspect); the second is a large synthetic dataset, AiC, generated in computer graphics with data extracted from the GTA video game, that contains 3D data of occluded objects by construction. Results are impressive and outperform any other previous proposal. This result could be an initial step to many further researches to recognize people and their behavior in an open crowded world. Highlights • We propose a novel Adversarial Network that solves occlusions in pedestrian images. • Our reconstruction keeps both the appearance and the background coherent. • We condition the occluded body part restoration on pedestrian attributes. • We devise a new way for synthetically generating occlusion and visible pairs. • We provide a huge CG dataset for pedestrian attribute recognition in crowded areas. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the current retrieval algorithm for massive ship fault data retrieval in cloud computing, which does not support complex query, large retrieval cost and low precision, a massive ship fault data retrieval algorithm supporting complex query in cloud computing based on query performance prediction is proposed. Introducing the Lagrangian algorithm to preprocess the ship fault data information; combining the attribute values of the ship fault data and the context to calculate the similarity between different attributes, and identifying the same attribute according to the similarity; The attribute recognition result is queried by using multiple retrieval models, and the model complex query request is converted into a one-dimensional query key value, and the query key values obtained by the plurality of models are predicted, and the search result with the optimal prediction performance of the complex query mode is used as the final result. The experimental results show that the proposed algorithm has good retrieval accuracy in complex query mode, and it has less overhead than the current search algorithm. [ABSTRACT FROM AUTHOR]

Accurate class and attribute recognition is the critical technique to convert the unstructured product image data into structured knowledge base, which provides strong support for product design in the future. However, objects of different classes sharing similar attribute may vary a lot in visual appearances, making it challenging to accurately recognize the objects and their attributes. Different from the traditional multi-label image recognition, the attribute of the object, as high-level semantic information, is not corresponding to certain regions of the object and requires to learn more fine-grained features to represent the latent high level semantic information for the attribute across different object categories. Therefore, a self-supervised method called Deconstruction and Reconstruction Learning Network (DRLN) is proposed to solve the above problems in this paper. The DRLN tries to learn more fine-grained and local feature of the input product image by a self-supervised task, which deconstructs the input product images by randomly shuffling their local regions and further reconstructs the features of corresponding deconstructed images. Besides, the proposed model is optimized in an end-to-end manner by learning from multiple tasks, i.e., the multi-label classification task, the adversarial discrimination task, and the location alignment task. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts for multi-label learning problems on both our product image dataset and another public available attribute recognition dataset. To facilitate future research in this field, all the datasets and codes are directly available online. 1 1 https://github.com/Yong-DAI/DRLN. [ABSTRACT FROM AUTHOR]

This study considered heated metal mark attribute recognition based on compressed convolutional neural networks (CNNs) models. Based on our previous works, the heated metal mark image benchmark dataset was further expanded. State-of-the-art lightweight CNNs models were selected. Technologies of pruning, compressing, weight quantization were introduced and analyzed. Then, a multi-label model training method was devised. Moreover, the proposed models were deployed on Android devices. Finally, comprehensive experiments were evaluated. The results show that, with the fine-tuned compressed CNNs model, the recognition rate of attributes meta type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity were 0.803, 0.837, 0.825, 0.812, 0.883, 0.817 and 0.894, respectively. The best model obtained an overall performance of 0.823. Comparing with traditional CNNs, the adopted compressed multi-label model greatly improved the training efficiency and reduced the space occupation, with a relatively small decrease in recognition accuracy. The running time on Android devices was acceptable. It is shown that the proposed model is applicable for real time application and is convenient to implement on mobile or embedded devices scenarios.

Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art. [ABSTRACT FROM AUTHOR]

Toward weather condition recognition, we emphasize the importance of regional cues in this paper and address a few important problems regarding appropriate representation, its differentiation among regions, and weather-condition feature construction. Our major contribution is, first, to construct a multi-class benchmark data set containing 65 000 images from six common categories for sunny, cloudy, rainy, snowy, haze, and thunder weather. This data set also benefits weather classification and attribute recognition. Second, we propose a deep learning framework named region selection and concurrency model (RSCM) to help discover regional properties and concurrency. We evaluate RSCM on our multi-class benchmark data and another public data set for weather recognition. [ABSTRACT FROM AUTHOR]

Due to large span and complex construction technology, the portal section of tunnel is vulnerable to the threats of latent slope failures such as landslide, spalling and collapse. In the present paper, to ensure the construction safety of tunnel, the attribute recognition model of risk assessment for the slope stability at tunnel portal was established by using attribute mathematics theory. The integrated degree of rock mass, lithological feature, geological structural feature, weathering degree, seismic intensity, gradient, rainfall and construction factors were selected as the indices of the risk assessment for the stability slope at tunnel portal with grading criteria of each index. Moreover, the weights of the assessment indices were determined by the judgment matrix constructed from analytic hierarchy process. The attribute measurement functions were used to compute attribute measurement of single index and synthetic attribute measurement. The identification and classification of risk assessment for the slope stability at tunnel portal were assessed using the confidence criterion. For practical purposes, the stability of slope located in portal of an actual Tunnel, i.e., Jiefangcun Tunnel in Cheng-Lan Railway, was evaluated based on the previously established attribute recognition model. The corresponding risk treatment measures were then proposed based on the assessment results and monitor results to ensure the construction safety of tunnel portal. [ABSTRACT FROM AUTHOR]

Heated metal mark is an important trace to identify the cause of fire. However, traditional methods mainly focus on the knowledge of physics and chemistry for qualitative analysis and make it still a challenging problem. This paper presents a case study on attribute recognition of the heated metal mark image using computer vision and machine learning technologies. The proposed work is composed of three parts. Material is first generated. According to national standards, actual needs and feasibility, seven attributes are selected for research. Data generation and organization are conducted, and a small size benchmark dataset is constructed. A recognition model is then implemented. Feature representation and classifier construction methods are introduced based on deep convolutional neural networks. Finally, the experimental evaluation is carried out. Multi-aspect testings are performed with various model structures, data augments, training modes, optimization methods and batch sizes. The influence of parameters, recognitio efficiency and execution time are also analyzed. The results show that with a fine-tuned model, the recognition rate of attributes metal type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity are 0.925, 0.908, 0.835, 0.917, 0.928, 0.805 and 0.92, respectively. The proposed method recognizes the attribute of heated metal mark with preferable effect, and it can be used in practical application.