571 citations · 2.9k across the 98 of their papers we have counts for
99 papers
Divert More Attention to Vision-Language Object Tracking
Mingzhe Guo, Zhipeng Zhang, Liping Jing +2
Multimodal vision-language (VL) learning has noticeably pushed the tendency toward generic intelligence owing to emerging large foundation models. However, tracking, as a fundament…
Free-Form Composition Networks for Egocentric Action Recognition
Haoran Wang, Qinghua Cheng, Baosheng Yu +4
Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognit…
DIR-AS: Decoupling Individual Identification and Temporal Reasoning for Action Segmentation
Peiyao Wang, Haibin Ling
Fully supervised action segmentation works on frame-wise action recognition with dense annotations and often suffers from the over-segmentation issue. Existing works have proposed…
The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector
Caixia Zhou, Yaping Huang, Mengyang Pu +3
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voti…
CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network
Ruyi Lian, Haibin Ling
Estimating the 6-DoF pose of a rigid object from a single RGB image is a crucial yet challenging task. Recent studies have shown the great potential of dense correspondence-based s…
CCTV-Gun: Benchmarking Handgun Detection in CCTV Images
Srikar Yellapragada, Zhenghong Li, Kevin Bhadresh Doshi +6
Gun violence is a critical security problem, and it is imperative for the computer vision community to develop effective gun detection algorithms for real-world scenarios, particul…
The Cascaded Forward Algorithm for Neural Network Training
Gongpei Zhao, Tao Wang, Yidong Li +3
Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of de…
Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment
Libo Zhang, Wenzhang Zhou, Heng Fan +2
Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches ac…
Expanding Language-Image Pretrained Models for General Video Recognition
Bolin Ni, Houwen Peng, Minghao Chen +5
Contrastive language-image pretraining has shown great success in learning visual-textual joint representation from web-scale data, demonstrating remarkable "zero-shot" generalizat…
Local Context-Aware Active Domain Adaptation
Tao Sun, Cheng Lu, Haibin Ling
Active Domain Adaptation (ADA) queries the labels of a small number of selected target samples to help adapting a model from a source domain to a target domain. The local context o…
Domain Adaptation with Adversarial Training on Penultimate Activations
Tao Sun, Cheng Lu, Haibin Ling
Enhancing model prediction confidence on target data is an important objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore adversarial training on penultimat…
Prior Knowledge Guided Unsupervised Domain Adaptation
Tao Sun, Cheng Lu, Haibin Ling
The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an attractive technique in many real-world applications, though it also brings great challenges…
Surgical Phase Recognition in Laparoscopic Cholecystectomy
Yunfan Li, Vinayak Shenoy, Prateek Prasanna +3
Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis. In this report, we propose a Transformer-based method that utilizes…
Safe Self-Refinement for Transformer-based Domain Adaptation
Tao Sun, Cheng Lu, Tianshuo Zhang +1
Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a…
Unsupervised Deep Learning Meets Chan-Vese Model
Dihan Zheng, Chenglong Bao, Zuoqiang Shi +2
The Chan-Vese (CV) model is a classic region-based method in image segmentation. However, its piecewise constant assumption does not always hold for practical applications. Many im…
Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images
Gongyang Li, Zhi Liu, Dan Zeng +2
Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to the difference between optic…
EDTER: Edge Detection with Transformer
Mengyang Pu, Yaping Huang, Yuming Liu +2
Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features. However, local details are gradually…
Consistency and Diversity induced Human Motion Segmentation
Tao Zhou, Huazhu Fu, Chen Gong +4
Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster d…
Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation
Gongyang Li, Zhi Liu, Zhen Bai +2
Salient object detection in optical remote sensing images (ORSI-SOD) has been widely explored for understanding ORSIs. However, previous methods focus mainly on improving the detec…
Deep Probabilistic Graph Matching
He Liu, Tao Wang, Yidong Li +3
Most previous learning-based graph matching algorithms solve the \textit{quadratic assignment problem} (QAP) by dropping one or more of the matching constraints and adopting a rela…
Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images
Gongyang Li, Zhi Liu, Weisi Lin +1
In the computer vision community, great progresses have been achieved in salient object detection from natural scene images (NSI-SOD); by contrast, salient object detection in opti…
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
Liting Lin, Heng Fan, Zhipeng Zhang +2
Recently Transformer has been largely explored in tracking and shown state-of-the-art (SOTA) performance. However, existing efforts mainly focus on fusing and enhancing features ge…
Searching the Search Space of Vision Transformer
Minghao Chen, Kan Wu, Bolin Ni +5
Vision Transformer has shown great visual representation power in substantial vision tasks such as recognition and detection, and thus been attracting fast-growing efforts on manua…
Joint Graph Learning and Matching for Semantic Feature Correspondence
He Liu, Tao Wang, Yidong Li +3
In recent years, powered by the learned discriminative representation via graph neural network (GNN) models, deep graph matching methods have made great progresses in the task of m…