Reviewerly/exHarmony huggingface dataset:
Dual attention-based method for occluded person re-identification

Occlusion is unavoidable in real-world applications of person re-identification (ReID). To alleviate the occlusion problem, this work proposes the detection of the occluded and visible regions of the human body by suppressing the occluded region during feature generation and matching, and enhancing the significance of the visible region. This paper introduces a novel method based on pose-guided spatial attention (PGSA) and activation-based attention (AA) called dual-attention re-identification (DAReID). DAReID consists of a mask branch and a global branch and uses ResNet-50 as the backbone network. The mask branch uses PGSA to obtain the visible and occluded regions of a person and constructs pose guided coarse labels for the occluded region through keypoints of the human body, driving the network to obtain robust local features. The global branch obtains the visual activation levels of different regions through AA, and combines this with human pose information to define weighted local distances(WLD). The WLD learning strategy is applied to drive the network to learn new and more discriminative local features. Experimental results show that DAReID achieves comparable performance on the Market1501, DukeMTMC-reID, and CUHK-03 datasets. And on the Occluded-DukeMTMC dataset, DAReID outperforms the existing methods.
Efficient Selective Context Network for Accurate Object Detection

Single-stage detectors have gained great attention due to their high detection accuracy and real-time speed. To detect multi-scale objects, single-stage detectors make scale-aware predictions based on multiple pyramid layers. However, the insufficient context exploration in shallow pyramid layers leads to the detection accuracy of small objects being far from satisfactory. To tackle this problem, we propose a scheme to selectively extract multi-scale context with attention-adaptive weights. Specifically, we propose an efficient selective context network for accurate object detection. It incorporates an enhanced context module and a triple attention module. The enhanced context module consists of multi-branches to extract original-scale, small-scale, and large-scale contextual information. To make full use of this context and filter out noisy information, the triple attention module, which contains global-level, channel-level, and spatial-level attentions, is introduced to carry out selective context fusion. The two modules are easy to implement and can efficiently boost the accuracy of object detection. The performance of our method is validated on two benchmarks: PASCAL VOC and MS COCO. For a 512×512 input, our detector with VGG16 achieves competitive results (80.9 on the Pascal VOC 2012 test set in the case of single-scale inference without MS COCO pre-training). On the MS COCO test-dev set, our detector with ResNet101 outperforms RetinaNet500 by 2.5% AP in terms of overall performance and its speed is 48 milliseconds on a Titan XP GPU. As a result, ESCNet achieves a better trade-off between accuracy and speed.
Integrated 9Be+ multi-qubit gate device for the ion-trap quantum computer

We demonstrate the experimental realization of a two-qubit M{\o}lmer-S{\o}rensen gate on a magnetic field-insensitive hyperfine transition in $^9$Be$^+$ ions using microwave-near fields emitted by a single microwave conductor embedded in a surface-electrode ion trap. The design of the conductor was optimized to produce a high oscillating magnetic field gradient at the ion position. The measured gate fidelity is determined to be $98.2\pm1.2\,\%$ and is limited by technical imperfections, as is confirmed by a comprehensive numerical error analysis. The conductor design can potentially simplify the implementation of multi-qubit gates and represents a self-contained, scalable module for entangling gates within the quantum CCD architecture for an ion-trap quantum computer.
Network Representation Learning: From Traditional Feature Learning to Deep Learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field.
3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images

In this paper, we present an approach for identification of actions within depth action videos. First, we process the video to get motion history images (MHIs) and static history images (SHIs) corresponding to an action video based on the use of 3D Motion Trail Model (3DMTM). We then characterize the action video by extracting the Gradient Local Auto-Correlations (GLAC) features from the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs and GLAC features from SHIs are concatenated to obtain a representation vector for action. Finally, we perform the classification on all the action samples by using the l2-regularized Collaborative Representation Classifier (l2-CRC) to recognize different human actions in an effective way. We perform evaluation of the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD. Through experimental results, we observe that the proposed method performs superior to other approaches.
The impact of using different annotation schemes on named entity recognition

Named entity recognition (NER) is a subfield of information extraction, which aims to detect and classify predefined named entities (e.g., people, locations, organizations, etc.) in a body of text. In the literature, many researchers have studied the application of different machine learning models and features to NER. However, few research efforts have been devoted to studying annotation schemes used to label multi-token named entities. In this research, we studied seven annotation schemes (IO, IOB, IOE, IOBES, BI, IE, and BIES) and their impact on the task of NER using five different classifiers. Our experiment was conducted on an in–house dataset that consists of 27 medical Arabic articles with more than 62,000 tokens. The IO annotation scheme outperformed other schemes with an F-measure score of 84.44%. The closest competitor is the BIES scheme, which scored 72.78%. The rest of the schemes’ scores ranged from 60.38% to 69.18%. Although the IO scheme achieved the best results, comparing it to the other schemes is not reasonable because it cannot identify consecutive entities, which the other schemes can do. Therefore, we also investigated the ability of recognizing consecutive entities and provided an analysis of the running-time complexity.
Low Cost Edge Sensing for High Quality Demosaicking

Digital cameras that use Color Filter Arrays (CFA) entail a demosaicking procedure to form full RGB images. As today's camera users generally require images to be viewed instantly, demosaicking algorithms for real applications must be fast. Moreover, the associated cost should be lower than the cost saved by using CFA. For this purpose, we revisit the classical Hamilton-Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Inspired by HA's strength and weakness, we design a very low cost edge sensing scheme. Briefly, it guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 28 demosaicking algorithms by running their open source codes on benchmark datasets. Compared to methods of similar computational cost, our method achieves substantially higher accuracy, Whereas compared to methods of similar accuracy, our method has significantly lower cost. Moreover, on test images of currently popular resolution, the quality of our algorithm is comparable to top performers, whereas its speed is tens of times faster.
Random Forest With Learned Representations for Semantic Segmentation

In this work, we present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation. Typical filters (kernels) have predetermined shapes and sparsities and learn only weights. A few feature extraction methods fix weights and learn only shapes and sparsities. These predetermined constraints restrict learning and extracting optimal features. To overcome this limitation, we propose an unconstrained representation that is able to extract optimal features by learning weights, shapes, and sparsities. We, then, present the random forest framework that learns the flexible filters using an iterative optimization algorithm and segments input images using the learned representations. We demonstrate the effectiveness of the proposed method using a hand segmentation dataset for hand-object interaction and using two semantic segmentation datasets. The results show that the proposed method achieves real-time semantic segmentation using limited computational and memory resources.
Multi-Domain Pose Network for Multi-Person Pose Estimation and Tracking

Multi-person human pose estimation and tracking in the wild is important and challenging. For training a powerful model, large-scale training data are crucial. While there are several datasets for human pose estimation, the best practice for training on multi-dataset has not been investigated. In this paper, we present a simple network called Multi-Domain Pose Network (MDPN) to address this problem. By treating the task as multi-domain learning, our methods can learn a better representation for pose prediction. Together with prediction heads fine-tuning and multi-branch combination, it shows significant improvement over baselines and achieves the best performance on PoseTrack ECCV 2018 Challenge without additional datasets other than MPII and COCO.
A Hybrid Intelligent Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet

In this paper, a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents.The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach (SAMMZWA).Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach.The SAMMZWA approach embeds and detects the watermark logically without altering the original text document.The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering, SAMMZWA has been implemented and validated with attacked Arabic text.Experiments were performed on four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks.The experimental results show that our method is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.
Recalibrating 3D ConvNets With Project &amp; Excite

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging. Recently, computational blocks termed squeeze and excitation (SE) have been introduced to recalibrate F-CNN feature maps both channel- and spatial-wise, boosting segmentation performance while only minimally increasing the model complexity. So far, the development of SE blocks has focused on 2D architectures. For volumetric medical images, however, 3D F-CNNs are a natural choice. In this article, we extend existing 2D recalibration methods to 3D and propose a generic compress-process-recalibrate pipeline for easy comparison of such blocks. We further introduce Project & Excite (PE) modules, customized for 3D networks. In contrast to existing modules, Project & Excite does not perform global average pooling but compresses feature maps along different spatial dimensions of the tensor separately to retain more spatial information that is subsequently used in the excitation step. We evaluate the modules on two challenging tasks, whole-brain segmentation of MRI scans and whole-body segmentation of CT scans. We demonstrate that PE modules can be easily integrated into 3D F-CNNs, boosting performance up to 0.3 in Dice Score and outperforming 3D extensions of other recalibration blocks, while only marginally increasing the model complexity. Our code is publicly available on https://github.com/ai-med/squeezeandexcitation.
Bright On-Demand Source of Antibunched Microwave Photons Based on Inelastic Cooper Pair Tunneling

The ability to generate single photons is not only an ubiquitous tool for scientific exploration with applications ranging from spectroscopy and metrology to quantum computing, but also an important proof of the underlying quantum nature of a physical process. In the microwave regime, emission of anti-bunched radiation has so far relied on coherent control of Josephson qubits, where precisely calibrated microwave pulses are needed, and the achievable bandwidth is limited by the anharmonicity of the qubit. Here, we demonstrate the operation of a bright on-demand source of quantum microwave radiation capable of emitting anti-bunched photons based on inelastic Cooper pair tunneling and driven by a simple DC voltage bias. It is characterized by its normalized second order correlation function of $g^{(2)}(0)\approx0.43$ corresponding to anti-bunching in the single photon regime. Our source can be triggered and its emission rate is tunable in situ exceeding rates obtained with current microwave single photon sources by more than one order of magnitude.
How the Presence and Size of Static Peripheral Blur Affects Cybersickness in Virtual Reality

Cybersickness (CS) is one of the challenges that has hindered the widespread adoption of Virtual Reality and its applications. Consequently, a number of studies have focused on extensively understanding and reducing CS. Inspired by previous work that has sought to reduce CS using foveated rendering and Field of View (FOV) restrictions, we investigated how the presence and size of a static central window in peripheral FOV blurring affects CS. To facilitate this peripheral FOV blur, we applied a Gaussian blur effect in the display peripheral region, provisioning a full-resolution central window. Thirty participants took part in a three-session, within-subjects experiment, performing search and spatial updating tasks in a first-person, slow-walking, maze-traveling scenario. Two different central window sizes (small and large) were tested against a baseline condition that didn’t feature display peripheral blurring. Results revealed that the baseline condition produced higher levels of CS than both conditions with a central window. While there were no significant differences between the small and large windows, we observed interaction effects suggesting an influence of window size on “adaptation to CS.” When the central window is small, adaptation to CS seems to take more time but is more pronounced. The interventions had no effect on spatial updating and presence, but were detectable when the blurred area was larger (small central window). Lower sickness levels observed in both window conditions supports the use of peripheral FOV blurring to reduce CS, reducing our dependence on eye tracking. This being said, researchers must strive to find the right balance between window size and detectability to ensure seamless virtual experiences.
Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images

Automatic ship detection in optical remote-sensing (ORS) images has wide applications in civil and military fields. Research on ship detection in ORS images started late compared to synthetic aperture radar (SAR) images, and it is difficult for traditional image-processing algorithms to achieve high accuracy. Therefore, we propose a ship-detection method based on a deep convolutional neural network that is modified from YOLOv3. We call it fused features and rebuilt (FFR) YOLOv3. We tried some improvements to enhance its performance in ship-detection regions. We added a squeeze-and-excitation (SE) structure to the backbone network to strengthen the ability to extract features. Through a large number of experiments, we optimized the backbone network to improve the speed. We improved the multi-scale detection of YOLOv3 by fusing multi-scale feature maps and regenerating them with a high-resolution network, which can improve the accuracy of detection and location. We used the public HRSC2016 ship-detection dataset and remote-sensing images collected from Google Earth to train, test, and verify our network, which reached a detection speed of about 27 frames per second (fps) on an NVIDIA RTX2080ti, with recall (R) = 95.32% and precision (P) = 95.62%. Experiments show that our network has better accuracy and speed than other methods. In addition, it has strong robustness and can adapt to complex environments like inshore ship detection.
A Performance Evaluation of Loss Functions for Deep Face Recognition

Face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of Computer Vision including face recognition. The loss function is used on the top of CNN to judge the goodness of any network. In this paper, we present a performance comparison of different loss functions such as Cross-Entropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. The experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing.
LDOP: local directional order pattern for robust face retrieval

The local descriptors have gained wide range of attention due to their enhanced discriminative abilities. It has been proved that the consideration of multi-scale local neighborhood improves the performance of the descriptor, though at the cost of increased dimension. This paper proposes a novel method to construct a local descriptor using multi-scale neighborhood by finding the local directional order among the intensity values at different scales in a particular direction. Local directional order is the multi-radius relationship factor in a particular direction. The proposed local directional order pattern (LDOP) for a particular pixel is computed by finding the relationship between the center pixel and local directional order indexes. It is required to transform the center value into the range of neighboring orders. Finally, the histogram of LDOP is computed over whole image to construct the descriptor. In contrast to the state-of-the-art descriptors, the dimension of the proposed descriptor does not depend upon the number of neighbors involved to compute the order; it only depends upon the number of directions. The introduced descriptor is evaluated over the image retrieval framework and compared with the state-of-the-art descriptors over challenging face databases such as PaSC, LFW, PubFig, FERET, AR, AT&T, and ExtendedYale. The experimental results confirm the superiority and robustness of the LDOP descriptor.
ICycleGAN: Single image dehazing based on iterative dehazing model and CycleGAN

The current competitive approaches to restoring haze-free images are mainly based on physical models and learning methods. Maintaining detail information of the image while thoroughly removing fog is a challenging task in single-image dehazing. In this paper, by embedding an iterative dehazing model into the generative process of the Cycle-Consistent Adversarial Network (CycleGAN), we propose a model named ICycleGAN that maintains the defogging thoroughness of the learning-based dehazing method while retaining the good fidelity of the physical model-based dehazing method. Moreover, the proposed ICycleGAN does not require pairs of hazy and relative haze-free images for training. In addition, we develop a detail information-consistency loss that preserves more textural details and color information; this loss is obtained based on the physical features of the hazy image. To recover the high-resolution images, we enlarge the generated images using rational fractal interpolation, which restores fine textures and sharp edges. Extensive comparison results show that the proposed method produces high-quality clear images that are both quantitatively and qualitatively competitive with other state-of-the-art methods.
Mutual Learning and Feature Fusion Siamese Networks for Visual Object Tracking

Recently Siamese-based trackers have shown their outstanding performance in visual object tracking community. But they seldom pay attention to the inter-branch interaction as well as intra-branch feature fusion from different convolution layers. In this paper, we build up a comprehensive Siamese network which consists of a mutual learning subnetwork (M-net) and a feature fusion subnetwork (F-net), to realize object tracking. Each of them is a Siamese network with special functions. M-net is designed to help the two branches mine the dependencies from each other, thus the object template is adaptively updated to a certain extent. F-net fuses different levels of convolutional features for full usage of spatial and semantic information. We also design a global-local channel attention (GLCA) module in F-net to capture the channel dependencies for a proper feature fusion. Our method takes ResNet as feature extractor and is trained offline in an end-to-end style. We evaluate our method in several famous benchmarks such as OTB2013, OTB2015, VOT2015, VOT2016, NFS and TC128. Extensive experimental results demonstrate our method achieves competitive results while maintaining a considerable real-time speed.
Identification of Colon Cancer Using Multi-Scale Feature Fusion Convolutional Neural Network Based on Shearlet Transform

Colon cancer identification is of great significance in medical diagnosis. Real-time, objective and accurate inspection results will facilitate medical professionals to explore symptomatic treatment promptly. However, the existing methods depend on hand-crafted features which require extensive professional expertise and long inspection period. Therefore, we propose a multi-scale feature fusion convolutional neural network (MFF-CNN) based on shearlet transform to identify histopathological image of colon cancer. The characteristic of the framework is the shearlet coefficients of histopathological image in multiple decomposition scales were extracted as supplementary feature which were also fed to the network with the original pathological image. After feature learning and feature fusion, the MFF-CNN based on shearlet transform can achieve the identification accuracy of 96% and average F-1 score of 0.9594 for colorectal adenocarcinoma epithelium (TUM) and normal colon mucosa (NORM). The false negative rate and false positive rate can be reduced to 5.5% and 2.5%, respectively. The superior performance of the network opens a new perspectives for real-time, objective and accurate diagnosis of cancer.