420 citations · 2.4k across the 126 of their papers we have counts for
27 papers · 1 filter
Interactive Fusion of Multi-level Features for Compositional Activity Recognition
Rui Yan, Lingxi Xie, Xiangbo Shu +1
To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated. However, these features are difficu…
UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification
Tianyu Zhang, Lingxi Xie, Longhui Wei +4
The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a no…
Privileged Knowledge Distillation for Online Action Detection
Peisen Zhao, Lingxi Xie, Ya Zhang +2
Online Action Detection (OAD) in videos is proposed as a per-frame labeling task to address the real-time prediction tasks that can only obtain the previous and current video frame…
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
Haohang Xu, Xiaopeng Zhang, Hao Li +3
Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations…
Batch Normalization with Enhanced Linear Transformation
Yuhui Xu, Lingxi Xie, Cihang Xie +5
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data…
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations
Xinyue Huo, Lingxi Xie, Longhui Wei +6
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial…
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?
Longhui Wei, Lingxi Xie, Jianzhong He +5
Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning. Pre-trained on ImageNet, some self-supervised algorithms reported h…
Omni-GAN: On the Secrets of cGANs and Beyond
Peng Zhou, Lingxi Xie, Bingbing Ni +2
The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the ri…
One-bit Supervision for Image Classification
Hengtong Hu, Lingxi Xie, Zewei Du +2
This paper presents one-bit supervision, a novel setting of learning from incomplete annotations, in the scenario of image classification. Instead of training a model upon the accu…
Reinforced Axial Refinement Network for Monocular 3D Object Detection
Lijie Liu, Chufan Wu, Jiwen Lu +3
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. This is an ill-posed problem with a major difficulty lying in the inf…
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap
Lingxi Xie, Xin Chen, Kaifeng Bi +8
Neural architecture search (NAS) has attracted increasing attentions in both academia and industry. In the early age, researchers mostly applied individual search methods which sam…
Corner Proposal Network for Anchor-free, Two-stage Object Detection
Kaiwen Duan, Lingxi Xie, Honggang Qi +3
The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a numb…
Social Adaptive Module for Weakly-supervised Group Activity Recognition
Rui Yan, Lingxi Xie, Jinhui Tang +2
This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet…
Universal-to-Specific Framework for Complex Action Recognition
Peisen Zhao, Lingxi Xie, Ya Zhang +1
Video-based action recognition has recently attracted much attention in the field of computer vision. To solve more complex recognition tasks, it has become necessary to distinguis…
GOLD-NAS: Gradual, One-Level, Differentiable
Kaifeng Bi, Lingxi Xie, Xin Chen +2
There has been a large literature of neural architecture search, but most existing work made use of heuristic rules that largely constrained the search flexibility. In this paper,…
Discretization-Aware Architecture Search
Yunjie Tian, Chang Liu, Lingxi Xie +2
The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods. These methods optimize a super-network with all possible edges and operation…
Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks
Peng Zhou, Lingxi Xie, Xiaopeng Zhang +2
Conditional Generative Adversarial Networks (cGAN) were designed to generate images based on the provided conditions, \eg, class-level distributions. However, existing methods have…
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation
Xinyue Huo, Lingxi Xie, Jianzhong He +2
In medical image analysis, semi-supervised learning is an effective method to extract knowledge from a small amount of labeled data and a large amount of unlabeled data. This paper…
Unsupervised Person Re-identification via Softened Similarity Learning
Yutian Lin, Lingxi Xie, Yu Wu +2
Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information and…
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio
Zhengsu Chen, Jianwei Niu, Lingxi Xie +3
Automatic designing computationally efficient neural networks has received much attention in recent years. Existing approaches either utilize network pruning or leverage the networ…
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
Xin Chen, Lingxi Xie, Jun Wu +3
Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-netw…
Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing
Hengtong Hu, Lingxi Xie, Richang Hong +1
In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vi…
Circumventing Outliers of AutoAugment with Knowledge Distillation
Longhui Wei, An Xiao, Lingxi Xie +3
AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper se…
Bottom-Up Temporal Action Localization with Mutual Regularization
Peisen Zhao, Lingxi Xie, Chen Ju +3
Recently, temporal action localization (TAL), i.e., finding specific action segments in untrimmed videos, has attracted increasing attentions of the computer vision community. Stat…