Disentangled Sample Guidance Learning for Unsupervised Person Re-Identification.

Unsupervised person re-identification (Re-ID) is challenging due to the lack of ground truth labels. Most existing methods employ iterative clustering to generate pseudo labels for unlabeled training data to guide the learning process. However, how to select samples that are both associated with high-confidence pseudo labels and hard (discriminative) enough remains a critical problem.

Scavenger Receptor C1 Mediates Toxicity of Binary Toxin from to Ag55 Cells.

harboring Binary (BinA and BinB) toxins is highly toxic against and mosquito larvae. The Ag55 cell line is a suitable model for investigating the mode of Bin toxin action. Based on the low-levels of α-glycosidase Agm3 mRNA in Ag55 cells and the absence of detectable Agm3 proteins, we hypothesized that a scavenger receptor could be mediating Bin cytotoxicity.

Toward High Quality Multi-Object Tracking and Segmentation Without Mask Supervision.

Recently studies have shown the potential of weakly supervised multi-object tracking and segmentation, but the drawbacks of coarse pseudo mask label and limited utilization of temporal information remain to be unresolved. To address these issues, we present a framework that directly uses box label to supervise the segmentation network without resorting to pseudo mask label. In addition, we propose to fully exploit the temporal information from two perspectives.

Towards Unified Robustness Against Both Backdoor and Adversarial Attacks.

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples and (2) for an infected model, its adversarial examples have similar features as the triggered images.

Transformer Based Pluralistic Image Completion With Reduced Information Loss.

Transformer based methods have achieved great success in image inpainting recently. However, we find that these solutions regard each pixel as a token, thus suffering from an information loss issue from two aspects: 1) They downsample the input image into much lower resolutions for efficiency consideration. 2) They quantize 256 RGB values to a small number (such as 512) of quantized color values.

Robust Model Watermarking for Image Processing Networks via Structure Consistency.

The intellectual property of deep networks can be easily "stolen" by surrogate model attack. There has been significant progress in protecting the model IP in classification tasks. However, little attention has been devoted to the protection of image processing models.

PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition.

Notwithstanding the prominent performance shown in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbations. In this paper, we delve into boosting the general robustness of point cloud recognition, proposing Point-Cloud Contrastive Adversarial Training (PointCAT). The main intuition of PointCAT is encouraging the target recognition model to narrow the decision gap between clean point clouds and corrupted point clouds by devising feature-level constraints rather than logit-level constraints.

Adversarial Attack and Defense in Deep Ranking.

Deep Neural Network classifiers are vulnerable to adversarial attacks, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.

A Novel Compute-and-Forward Relaying Method for Multi-Antenna Wireless Relay Networks.

Compute-and-Forward (CoF) is an innovative physical layer network coding strategy, designed to enable receivers in wireless communications to effectively utilize interference. The key idea of CoF is to implement integer combinations based on the codewords from multiple transmitters, rather than decoding individual source codewords. Although CoF is widely used in wireless relay networks, there are still some problems to be solved, such as rank failure, single antenna reception, and the shortest vector problem.

Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map.

Counting objects in crowded scenes remains a challenge to computer vision. The current deep learning based approach often formulate it as a Gaussian density regression problem. Such a brute-force regression, though effective, may not consider the annotation displacement properly which arises from the human annotation process and may lead to different distributions.

Dynamic Release Characteristics and Kinetics of a Persulfate Sustained-Release Material.

Sustained-release materials are increasingly being used in the delivery of oxidants for in situ chemical oxidation (ISCO) for groundwater remediation. Successful implementation of sustained-release materials depends on a clear understanding of the mechanism and kinetics of sustained release. In this research, a columnar sustained-release material (PS@PW) was prepared with paraffin wax and sodium persulfate (PS), and column experiments were performed to investigate the impacts of the PS@PW diameter and PS/PW mass ratio on PS release.

Deep Image Matting With Sparse User Interactions.

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous.

Representing Multimodal Behaviors With Mean Location for Pedestrian Trajectory Prediction.

Representing multimodal behaviors is a critical challenge for pedestrian trajectory prediction. Previous methods commonly represent this multimodality with multiple latent variables repeatedly sampled from a latent space, encountering difficulties in interpretable trajectory prediction. Moreover, the latent space is usually built by encoding global interaction into future trajectory, which inevitably introduces superfluous interactions and thus leads to performance reduction.

ContextLoc++: A Unified Context Model for Temporal Action Localization.

Effectively tackling the problem of temporal action localization (TAL) necessitates a visual representation that jointly pursues two confounding goals, i.e., fine-grained discrimination for temporal localization and sufficient visual invariance for action classification.

Instance Motion Tendency Learning for Video Panoptic Segmentation.

Video panoptic segmentation is an important but challenging task in computer vision. It not only performs panoptic segmentation of each frame, but also associates the same instance across adjacent frames. Due to the lack of temporal coherence modeling, most existing approaches often generate identity switches during instance association, and they cannot handle ambiguous segmentation boundaries caused by motion blur.

Time-resolved simulation of blood flow through left anterior descending coronary artery: effect of varying extent of stenosis on hemodynamics.

Background And Objectives: Real-time blood flow variation is crucial for understanding the dynamic development of coronary atherosclerosis. The main objective of this study is to investigate the effect of varying extent of stenosis on the hemodynamic features in left anterior descending coronary artery.

Methods: Various Computational fluid dynamics (CFD) models were constructed with patient-specific CT image data, using actual fractional flow reserve (FFR) as boundary conditions to provide a real-time quantitative description of hemodynamic properties.

Semantic Probability Distribution Modeling for Diverse Semantic Image Synthesis.

Semantic image synthesis, translating semantic layouts to photo-realistic images, is a one-to-many mapping problem. Though impressive progress has been recently made, diverse semantic synthesis that can efficiently produce semantic-level or even instance-level multimodal results, still remains a challenge. In this article, we propose a novel diverse semantic image synthesis framework from the perspective of semantic class distributions, which naturally supports diverse generation at both semantics and instance level.

Poison Ink: Robust and Invisible Backdoor Attack.

Recent research shows deep neural networks are vulnerable to different types of attacks, such as adversarial attacks, data poisoning attacks, and backdoor attacks. Among them, backdoor attacks are the most cunning and can occur in almost every stage of the deep learning pipeline. Backdoor attacks have attracted lots of interest from both academia and industry.

Adaptive Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization.

Weakly-supervised temporal action localization (W-TAL) aims to classify and localize all action instances in untrimmed videos under only video-level supervision. Without frame-level annotations, it is challenging for W-TAL methods to clearly distinguish actions and background, which severely degrades the action boundary localization and action proposal scoring. In this paper, we present an adaptive two-stream consensus network (A-TSCN) to address this problem.

A Fast Multi-Scale Generative Adversarial Network for Image Compressed Sensing.

Recently, deep neural network-based image compressed sensing methods have achieved impressive success in reconstruction quality. However, these methods (1) have limitations in sampling pattern and (2) usually have the disadvantage of high computational complexity. To this end, a fast multi-scale generative adversarial network (FMSGAN) is implemented in this paper.

E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion.

This paper studies the problem of StyleGAN inversion, which plays an essential role in enabling the pretrained StyleGAN to be used for real image editing tasks. The goal of StyleGAN inversion is to find the exact latent code of the given image in the latent space of StyleGAN. This problem has a high demand for quality and efficiency.

Anopheles gambiae strain (Ag55) cultured cells originated from Anopheles coluzzii and are phagocytic with hemocyte-like gene expression.

Anopheles gambiae and Anopheles coluzzii are closely related species that are predominant vectors of malaria in Africa. Recently, A. gambiae form M was renamed A.

LncRNA XIST Contributes to Cisplatin Resistance of Lung Cancer Cells by Promoting Cellular Glycolysis through Sponging miR-101-3p.

Background: Non-small-cell lung carcinoma is one of the most frequently diagnosed cancers. Cisplatin (CDDP) is a currently applied standard anticancer agent for advanced lung cancers. Although effectively clinical response was achieved initially, a large fraction of lung cancer patients developed cisplatin resistance.