915 citations · 3.2k across the 107 of their papers we have counts for
24 papers · 1 filter
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers
Lin Liu, Shanxin Yuan, Jianzhuang Liu +3
We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements (such as rains, snow, and moire patterns) that vary in successive frame…
Exploring Complicated Search Spaces with Interleaving-Free Sampling
Yunjie Tian, Lingxi Xie, Jiemin Fang +3
The existing neural architecture search algorithms are mostly working on search spaces with short-distance connections. We argue that such designs, though safe and stable, obstacle…
General Greedy De-bias Learning
Xinzhe Han, Shuhui Wang, Chi Su +2
Neural networks often make predictions relying on the spurious correlations from the datasets rather than the intrinsic properties of the task of interest, facing sharp degradation…
NeuSample: Neural Sample Field for Efficient View Synthesis
Jiemin Fang, Lingxi Xie, Xinggang Wang +3
Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is s…
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
Yunjie Tian, Lingxi Xie, Xiaopeng Zhang +6
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by…
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes
Jiazhong Cen, Zenkun Jiang, Lingxi Xie +3
Anomaly segmentation is a crucial task for safety-critical applications, such as autonomous driving in urban scenes, where the goal is to detect out-of-distribution (OOD) objects w…
DocScanner: Robust Document Image Rectification with Progressive Learning
Hao Feng, Wengang Zhou, Jiajun Deng +2
Compared with flatbed scanners, portable smartphones provide more convenience for physical document digitization. However, such digitized documents are often distorted due to uncon…
Rectifying the Shortcut Learning of Background for Few-Shot Learning
Xu Luo, Longhui Wei, Liangjian Wen +4
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time…
Bag of Instances Aggregation Boosts Self-supervised Distillation
Haohang Xu, Jiemin Fang, Xiaopeng Zhang +5
Recent advances in self-supervised learning have experienced remarkable progress, especially for contrastive learning based methods, which regard each image as well as its augmenta…
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation
Bowen Shi, Xiaopeng Zhang, Haohang Xu +4
Collecting annotated data for semantic segmentation is time-consuming and hard to scale up. In this paper, we for the first time propose a unified framework, termed as Multi-Datase…
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
Xue Yang, Xiaojiang Yang, Jirui Yang +4
Existing rotated object detectors are mostly inherited from the horizontal detection paradigm, as the latter has evolved into a well-developed area. However, these detectors are di…
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task
Longhui Wei, Lingxi Xie, Wengang Zhou +2
Recently, self-supervised learning methods have achieved remarkable success in visual pre-training task. By simply pulling the different augmented views of each image together or o…
What Is Considered Complete for Visual Recognition?
Lingxi Xie, Xiaopeng Zhang, Longhui Wei +2
This is an opinion paper. We hope to deliver a key message that current visual recognition systems are far from complete, i.e., recognizing everything that human can recognize, yet…
Towards Compact CNNs via Collaborative Compression
Yuchao Li, Shaohui Lin, Jianzhuang Liu +7
Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression. However, these two techniques are traditionally deployed in…
A Fourier-based Framework for Domain Generalization
Qinwei Xu, Ruipeng Zhang, Ya Zhang +2
Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data. Domain generalization aims at tackl…
Semi-supervised Contrastive Learning with Similarity Co-calibration
Yuhang Zhang, Xiaopeng Zhang, Robert. C. Qiu +3
Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed as Semi-supervised Contrastive Lea…
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
Hu Cao, Yueyue Wang, Joy Chen +4
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis. Especially, the deep neural networks based on U-shaped architecture…
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens
Jiemin Fang, Lingxi Xie, Xinggang Wang +3
Transformers have offered a new methodology of designing neural networks for visual recognition. Compared to convolutional networks, Transformers enjoy the ability of referring to…
CondenseNet V2: Sparse Feature Reactivation for Deep Networks
Le Yang, Haojun Jiang, Ruojin Cai +4
Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet has shown that this mecha…
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
Chen Ju, Peisen Zhao, Siheng Chen +3
Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level action category labels. Most of previous methods ignore the incomp…
Visformer: The Vision-friendly Transformer
Zhengsu Chen, Lingxi Xie, Jianwei Niu +3
The past year has witnessed the rapid development of applying the Transformer module to vision problems. While some researchers have demonstrated that Transformer-based models enjo…
Spatiotemporal Transformer for Video-based Person Re-identification
Tianyu Zhang, Longhui Wei, Lingxi Xie +4
Recently, the Transformer module has been transplanted from natural language processing to computer vision. This paper applies the Transformer to video-based person re-identificati…
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
Wei Gao, Fang Wan, Xingjia Pan +5
Weakly supervised object localization (WSOL) is a challenging problem when given image category labels but requires to learn object localization models. Optimizing a convolutional…
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Xue Yang, Junchi Yan, Qi Ming +3
Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design. In this paper, we propose a novel…