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This research proposes a deep learning method for classifying student behavior in classrooms that follow the professional learning community teaching approach. We collected data on five student activities: hand-raising, interacting, sitting, turning around, and writing. We used the sum of absolute differences (SAD) in the LUV color space to detect scene changes. The K-means algorithm was then applied to select keyframes using the computed SAD. Next, we extracted features using multiple pretrained deep learning models from the convolutional neural network family. The pretrained models considered were InceptionV3, ResNet50V2, VGG16, and EfficientNetB7. We leveraged feature fusion, incorporating optical flow features and data augmentation techniques, to increase the necessary spatial features of selected keyframes. Finally, we classified the students’ behavior using a deep sequence model based on the bidirectional long short-term memory network with an attention mechanism (BiLSTM-AT). The proposed method with the BiLSTM-AT model can recognize behaviors from our dataset with high accuracy, precision, recall, and F1-scores of 0.97, 0.97, and 0.97, respectively. The overall accuracy was 96.67%. This high efficiency demonstrates the potential of the proposed method for classifying student behavior in classrooms.

Aerial images are important for monitoring land cover and land resource management. An aerial imaging source which keeps its position at a higher altitude, and which has a considerable duration of airtime, employs wireless communications for sending images to relevant receivers. An aerial image must be transmitted from the image source to a ground station where it can be stored and analyzed. Due to transmission errors, aerial images which are received from an image transmitter contain distortions which can affect the quality of the images, causing noise, color shifts, and other issues that can impact the accuracy of semantic segmentation and the usefulness of the information contained in the images. Current semantic segmentation methods discard distorted images, which makes the available dataset small or treats them as normal images, which causes poor segmentation results. This paper proposes a deep-learning-based semantic segmentation method for distorted aerial images. For different receivers, distortions occur differently, and by considering the receiver specificness of the distortions, the proposed method was able to grasp the acceptability for a distorted image using semantic segmentation models trained with large aerial image datasets to build a combined model that can effectively segment a distorted aerial image which was received by an analog image receiver. Two combined deep learning models, an approximating model, and a segmentation model were trained combinedly to maximize the segmentation score for distorted images. The results showed that the combined learning method achieves higher intersection-over-union (IoU) scores than the results obtained by using only a segmentation model.

Palm vein recognition has received some considerable attention regarding its use in biometric identification. Palm vein characteristics offer a superior level of security and reliability in personal identification compared to extrinsic methods such as fingerprint, face, and palm print recognition, as vein patterns are difficult to duplicate and do not change throughout one’s lifetime. This study proposes both segmentation and recognition methods to enhance the authentication performance and achieve correct identification using palm vein features. First, we propose a segmentation model based on the U-Net model, enhanced with an attention gate, to effectively segment palm vein patterns. The incorporation of both the attention gate and residual block allows the segmentation model for the learning of the essential features required for specific segmentation tasks. The Hessian-based Jerman filtering method is used for ground-truth labeling. The segmentation model extracts the palm vein patterns and filters out the irrelevant and noisy pixels for the purpose of recognition. The efficient channel attention residual network is trained to learn discriminative features for personal identification using combined margin-based loss functions for palm vein recognition. The channel attention module enhances the useful information and suppresses irrelevant features in the feature maps, which overcomes the problem of rotation, position translation, and scale transformation, as well as improves the recognition rate. The combined loss function used in this study increases the similarity between the intra-class samples and the diversity between inter-class samples. The proposed recognition model achieved 100% accuracy for palm vein recognition and an equal error rate of 0.018 for palm vein verification.

With the industrial demand caused by multi-sensor image fusion, infrared and visible image fusion (IVIF) technology is flourishing. In recent years, scale decomposition methods have led the trend for feature extraction. Such methods, however, have low time efficiency. To address this issue, this paper proposes a simple yet effective IVIF approach via a feature-oriented dual-module complementary. Specifically, we analyze five classical operators comprehensively and construct the spatial gradient capture module (SGCM) and infrared brightness supplement module (IBSM). In the SGCM, three kinds of feature maps are obtained, respectively, by introducing principal component analysis, saliency, and proposing contrast estimation operators considered the relative differences of contrast information covered by the input images. These maps are later reconstructed through pyramidal transformation to obtain the predicted image. The IBSM is then proposed to refine the missing infrared thermal information in the predicted image. Among them, we improve the measurement operators applied to the exposure modalities, namely, the gradient of the grayscale images (2D gradient) and well-exposedness. The former is responsible for extracting fine details, and the latter is meant for locating brightness regions. Experiments performed on public datasets demonstrate that the proposed method outperforms nine state-of-the-art methods in terms of subjective visual and objective indicators.

Human action recognition (HAR) has gained popularity because of its various applications, such as human–object interaction [...]

The computer generation of poetry has been studied for more than a decade. Generating poetry on a human level is still a great challenge for the computer-generation process. We present a novel Transformer-XL based on a classical Chinese poetry model that employs a multi-head self-attention mechanism to capture the deeper multiple relationships among Chinese characters. Furthermore, we utilized the segment-level recurrence mechanism to learn longer-term dependency and overcome the context fragmentation problem. To automatically assess the quality of the generated poems, we also built a novel automatic evaluation model that contains a BERT-based module for checking the fluency of sentences and a tone-checker module to evaluate the tone pattern of poems. The poems generated using our model obtained an average score of 9.7 for fluency and 10.0 for tone pattern. Moreover, we visualized the attention mechanism, and it showed that our model learned the tone-pattern rules. All experiment results demonstrate that our poetry generation model can generate high-quality poems.

Deep-learning-based, cross-age face recognition has improved significantly in recent years. However, when using the discriminative method, it is still challenging to extract robust age-invariant features that can reduce the interference caused by age. In this paper, we propose a novel, effective, attention-based feature decomposition model, the age-invariant features extraction network, which can learn more discriminative feature representations and reduce the disturbance caused by aging. Our method uses an efficient channel attention block-based feature decomposition module to extract age-independent identity features from facial representations. Our end-to-end framework learns the age-invariant features directly, which is more convenient and can greatly reduce training complexity compared with existing multi-stage training methods. In addition, we propose a direct sum loss function to reduce the interference of age-related features. Our method achieves a comparable and stable performance. Experimental results demonstrate superior performance on four benchmarked datasets over the state-of-the-art. We obtain the relative improvements of 0.06%, 0.2%, and 2.2% on the cross-age datasets CACD-VS, AgeDB, and CALFW, respectively, and a relative 0.03% improvement on a general dataset LFW.

Research on facial recognition has recently been flourishing, which has led to the introduction of many robust methods. However, since the worldwide outbreak of COVID-19, people have had to regularly wear facial masks, thus making existing face recognition methods less reliable. Although normal face recognition methods are nearly complete, masked face recognition (MFR)—which refers to recognizing the identity of an individual when people wear a facial mask—remains the most challenging topic in this area. To overcome the difficulties involved in MFR, a novel deep learning method based on the convolutional block attention module (CBAM) and angular margin ArcFace loss is proposed. In the method, CBAM is integrated with convolutional neural networks (CNNs) to extract the input image feature maps, particularly of the region around the eyes. Meanwhile, ArcFace is used as a training loss function to optimize the feature embedding and enhance the discriminative feature for MFR. Because of the insufficient availability of masked face images for model training, this study used the data augmentation method to generate masked face images from a common face recognition dataset. The proposed method was evaluated using the well-known masked image version of LFW, AgeDB-30, CFP-FP, and real mask image MFR2 verification datasets. A variety of experiments confirmed that the proposed method offers improvements for MFR compared to the current state-of-the-art methods.

Human pose estimation is a problem that continues to be one of the greatest challenges in the field of computer vision. While the stacked structure of an hourglass network has enabled substantial progress in human pose estimation and key-point detection areas, it is largely used as a backbone network. However, it also requires a relatively large number of parameters and high computational capacity due to the characteristics of its stacked structure. Accordingly, the present work proposes a more lightweight version of the hourglass network, which also improves the human pose estimation performance. The new hourglass network architecture utilizes several additional skip connections, which improve performance with minimal modifications while still maintaining the number of parameters in the network. Additionally, the size of the convolutional receptive field has a decisive effect in learning to detect features of the full human body. Therefore, we propose a multidilated light residual block, which expands the convolutional receptive field while also reducing the computational load. The proposed residual block is also invariant in scale when using multiple dilations. The well-known MPII and LSP human pose datasets were used to evaluate the performance using the proposed method. A variety of experiments were conducted that confirm that our method is more efficient compared to current state-of-the-art hourglass weight-reduction methods.

Skeleton-based action recognition is a widely used task in action related research because of its clear features and the invariance of human appearances and illumination. Furthermore, it can also effectively improve the robustness of the action recognition. Graph convolutional networks have been implemented on those skeletal data to recognize actions. Recent studies have shown that the graph convolutional neural network works well in the action recognition task using spatial and temporal features of skeleton data. The prevalent methods to extract the spatial and temporal features purely rely on a deep network to learn from primitive 3D position. In this paper, we propose a novel action recognition method applying high-order spatial and temporal features from skeleton data, such as velocity features, acceleration features, and relative distance between 3D joints. Meanwhile, a method of multi-stream feature fusion is adopted to fuse these high-order features we proposed. Extensive experiments on Two large and challenging datasets, NTU-RGBD and NTU-RGBD-120, indicate that our model achieves the state-of-the-art performance.

We propose Identical-pair Adversarial Networks (iPANs) to solve image-to-image translation problems, such as aerial-to-map, edge-to-photo, de-raining, and night-to-daytime. Our iPANs rely mainly on the effectiveness of adversarial loss function and its network architectures. Our iPANs consist of two main networks, an image transformation network T and a discriminative network D. We use U-NET for the transformation network T and a perceptual similarity network, which has two streams of VGG16 that share the same weights for network D. Our proposed adversarial losses play a minimax game against each other based on a real identical-pair and a fake identical-pair distinguished by the discriminative network D; e.g. a discriminative network D considers two inputs as a real pair only when they are identical, otherwise a fake pair. Meanwhile, the transformation network T tries to persuade the discriminator network D that the fake pair is a real pair. We experimented on several problems of image-to-image translation and achieved results that are comparable to those of some existing approaches, such as pix2pix, and PAN.

The exemplar-based method is most frequently used in face sketch synthesis because of its efficiency in representing the nonlinear mapping between face photos and sketches. However, the sketches synthesized by existing exemplar-based methods suffer from block artifacts and blur effects. In addition, most exemplar-based methods ignore the training sketches in the weight representation process. To improve synthesis performance, a novel joint training model is proposed in this paper, taking sketches into consideration. First, we construct the joint training photo and sketch by concatenating the original photo and its sketch with a high-pass filtered image of their corresponding sketch. Then, an offline random sampling strategy is adopted for each test photo patch to select the joint training photo and sketch patches in the neighboring region. Finally, a novel locality constraint is designed to calculate the reconstruction weight, allowing the synthesized sketches to have more detailed information. Extensive experimental results on public datasets show the superiority of the proposed joint training model, both from subjective perceptual and the FaceNet-based face recognition objective evaluation, compared to existing state-of-the-art sketch synthesis methods.

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.