786 citations · 7.3k across the 175 of their papers we have counts for
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D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations
Sanath Narayan, Hisham Cholakkal, Munawar Hayat +3
This work proposes a weakly-supervised temporal action localization framework, called D2-Net, which strives to temporally localize actions using video-level supervision. Our main c…
Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
Wencheng Han, Xingping Dong, Fahad Shahbaz Khan +2
Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCor…
DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations
Wenhao Wang, Shengcai Liao, Fang Zhao +2
Existing person re-identification models often have low generalizability, which is mostly due to limited availability of large-scale labeled data in training. However, labeling lar…
Learning Efficient GANs for Image Translation via Differentiable Masks and co-Attention Distillation
Shaojie Li, Mingbao Lin, Yan Wang +3
Generative Adversarial Networks (GANs) have been widely-used in image translation, but their high computation and storage costs impede the deployment on mobile devices. Prevalent m…
Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection
Nian Liu, Ni Zhang, Ling Shao +1
How to effectively fuse cross-modal information is the key problem for RGB-D salient object detection. Early fusion and the result fusion schemes fuse RGB and depth information at…
Learning to Learn Variational Semantic Memory
Xiantong Zhen, Yingjun Du, Huan Xiong +3
In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores…
From Handcrafted to Deep Features for Pedestrian Detection: A Survey
Jiale Cao, Yanwei Pang, Jin Xie +2
Pedestrian detection is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed…
Normalization Techniques in Training DNNs: Methodology, Analysis and Application
Lei Huang, Jie Qin, Yi Zhou +3
Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various app…
Exploring global diverse attention via pairwise temporal relation for video summarization
Ping Li, Qinghao Ye, Luming Zhang +3
Video summarization is an effective way to facilitate video searching and browsing. Most of existing systems employ encoder-decoder based recurrent neural networks, which fail to e…
Group Whitening: Balancing Learning Efficiency and Representational Capacity
Lei Huang, Yi Zhou, Li Liu +2
Batch normalization (BN) is an important technique commonly incorporated into deep learning models to perform standardization within mini-batches. The merits of BN in improving a m…
A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability
Yi Zhou, Boyang Wang, Lei Huang +2
People with diabetes are at risk of developing an eye disease called diabetic retinopathy (DR). This disease occurs when high blood glucose levels cause damage to blood vessels in…
Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance
Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran +1
Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural info…
RGB-D Salient Object Detection: A Survey
Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng +2
Salient object detection (SOD), which simulates the human visual perception system to locate the most attractive object(s) in a scene, has been widely applied to various computer v…
SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation
Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal +3
Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage met…
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
Mang Ye, Jianbing Shen, David J. Crandall +2
Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Due to the large intra-class variations and cross-modality discrep…
Learning to Learn with Variational Information Bottleneck for Domain Generalization
Yingjun Du, Jun Xu, Huan Xiong +4
Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We…
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Tao Zhou, Huazhu Fu, Yu Zhang +4
Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Although many OS time prediction methods have bee…
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
Deng-Ping Fan, Ge-Peng Ji, Tao Zhou +4
Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy image…
Bifurcated backbone strategy for RGB-D salient object detection
Yingjie Zhai, Deng-Ping Fan, Jufeng Yang +4
Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features mee…
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
Ionut Cosmin Duta, Li Liu, Fan Zhu +1
This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales. PyConv contains a pyramid of kernels, where each level invo…
Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification
Yanan Wang, Shengcai Liao, Ling Shao
Person re-identification has seen significant advancement in recent years. However, the ability of learned models to generalize to unknown target domains still remains limited. One…
Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond
Wenhao Wang, Fang Zhao, Shengcai Liao +1
Unsupervised domain adaptation (UDA) for person re-identification is challenging because of the huge gap between the source and target domain. A typical self-training method is to…
Conditional Variational Image Deraining
Ying-Jun Du, Jun Xu, Xian-Tong Zhen +2
Image deraining is an important yet challenging image processing task. Though deterministic image deraining methods are developed with encouraging performance, they are infeasible…
Modeling and Enhancing Low-quality Retinal Fundus Images
Ziyi Shen, Huazhu Fu, Jianbing Shen +1
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have…