73 citations · 289 across the 37 of their papers we have counts for
37 papers
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection
Guodong Wang, Yunhong Wang, Jie Qin +3
Anomaly detection (AD), aiming to find samples that deviate from the training distribution, is essential in safety-critical applications. Though recent self-supervised learning bas…
Self-Paced Learning for Open-Set Domain Adaptation
Xinghong Liu, Yi Zhou, Tao Zhou +2
Domain adaptation tackles the challenge of generalizing knowledge acquired from a source domain to a target domain with different data distributions. Traditional domain adaptation…
Memory-aided Contrastive Consensus Learning for Co-salient Object Detection
Peng Zheng, Jie Qin, Shuo Wang +2
Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for f…
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection
Zhiheng Hu, Yongzhen Wang, Peng Li +3
Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in…
Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentation
Jie Qin, Jie Wu, Ming Li +3
Albeit with varying degrees of progress in the field of Semi-Supervised Semantic Segmentation, most of its recent successes are involved in unwieldy models and the lightweight solu…
Motion Sensitive Contrastive Learning for Self-supervised Video Representation
Jingcheng Ni, Nan Zhou, Jie Qin +4
Contrastive learning has shown great potential in video representation learning. However, existing approaches fail to sufficiently exploit short-term motion dynamics, which are cru…
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles
Guodong Wang, Yunhong Wang, Jie Qin +3
Video Anomaly Detection (VAD) is an important topic in computer vision. Motivated by the recent advances in self-supervised learning, this paper addresses VAD by solving an intuiti…
SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild
Jie Qin, Shuaihang Yuan, Jiaxin Chen +22
Sketch-based 3D shape retrieval (SBSR) is an important yet challenging task, which has drawn more and more attention in recent years. Existing approaches address the problem in a r…
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
Peng Zheng, Huazhu Fu, Deng-Ping Fan +5
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in na…
RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
Bing Wang, Zhengdi Yu, Bo Yang +5
We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds. Unlike occupancy fields…
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
Zichen Yang, Jie Qin, Di Huang
Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available. Existing appr…
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation
Jie Qin, Jie Wu, Xuefeng Xiao +2
Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet challenging computer vision task facilitating scene understanding and automatic driving. Most existi…
MovieNet-PS: A Large-Scale Person Search Dataset in the Wild
Jie Qin, Peng Zheng, Yichao Yan +3
Person search aims to jointly localize and identify a query person from natural, uncropped images, which has been actively studied over the past few years. In this paper, we delve…
TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification
Yichao Yan, Junjie Li, Shengcai Liao +3
Domain generalizable person re-identification aims to apply a trained model to unseen domains. Prior works either combine the data in all the training domains to capture domain-inv…
Efficient Person Search: An Anchor-Free Approach
Yichao Yan, Jinpeng Li, Jie Qin +2
Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. To achieve this goal, state-of-the-art models typically add a re-id bran…
Identity-aware Graph Memory Network for Action Detection
Jingcheng Ni, Jie Qin, Di Huang
Action detection plays an important role in high-level video understanding and media interpretation. Many existing studies fulfill this spatio-temporal localization by modeling the…
Exploring Visual Context for Weakly Supervised Person Search
Yichao Yan, Jinpeng Li, Shengcai Liao +4
Person search has recently emerged as a challenging task that jointly addresses pedestrian detection and person re-identification. Existing approaches follow a fully supervised set…
You Never Cluster Alone
Yuming Shen, Ziyi Shen, Menghan Wang +3
Recent advances in self-supervised learning with instance-level contrastive objectives facilitate unsupervised clustering. However, a standalone datum is not perceiving the context…
Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
Yichao Yan, Jie Qin1, Jiaxin Chen +4
Video-based person re-identification (re-ID) is an important research topic in computer vision. The key to tackling the challenging task is to exploit both spatial and temporal clu…
Learning Multi-Attention Context Graph for Group-Based Re-Identification
Yichao Yan, Jie Qin, Bingbing Ni +6
Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. However, most existing methods focus on…
Anchor-Free Person Search
Yichao Yan, Jinpeng Li, Jie Qin +5
Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and pe…
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Bing Wang, Changhao Chen, Zhaopeng Cui +8
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D loc…
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning
Zhiqiang Shen, Zechun Liu, Jie Qin +2
The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. A common practice for this task is to train a model…
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration
Zhiqiang Shen, Zechun Liu, Jie Qin +3
Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural netw…