21 citations · 71 across the 16 of their papers we have counts for
16 papers
Visual Prototype Conditioned Focal Region Generation for UAV-Based Object Detection
Wenhao Li, Zimeng Wu, Yu Wu +2
Unmanned aerial vehicle (UAV) based object detection is a critical but challenging task, when applied in dynamically changing scenarios with limited annotated training data. Layout…
ResWorld: Temporal Residual World Model for End-to-End Autonomous Driving
Jinqing Zhang, Zehua Fu, Zelin Xu +3
The comprehensive understanding capabilities of world models for driving scenarios have significantly improved the planning accuracy of end-to-end autonomous driving frameworks. Ho…
De-Simplifying Pseudo Labels to Enhancing Domain Adaptive Object Detection
Zehua Fu, Chenguang Liu, Yuyu Chen +3
Despite its significant success, object detection in traffic and transportation scenarios requires time-consuming and laborious efforts in acquiring high-quality labeled data. Ther…
GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object Detection
Jinqing Zhang, Yanan Zhang, Yunlong Qi +3
Bird's-Eye-View (BEV) representation has emerged as a mainstream paradigm for multi-view 3D object detection, demonstrating impressive perceptual capabilities. However, existing me…
Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation
Ye Du, Zehua Fu, Qingjie Liu
Recent attention has been devoted to the pursuit of learning semantic segmentation models exclusively from image tags, a paradigm known as image-level Weakly Supervised Semantic Se…
Improving Multi-Person Pose Tracking with A Confidence Network
Zehua Fu, Wenhang Zuo, Zhenghui Hu +2
Human pose estimation and tracking are fundamental tasks for understanding human behaviors in videos. Existing top-down framework-based methods usually perform three-stage tasks: h…
D: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos
Rui He, Zehua Fu, Qingjie Liu +2
Tracking multiple athletes in sports videos is a very challenging Multi-Object Tracking (MOT) task, since athletes often have the same appearance and are intimately covered with ea…
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Ye Du, Yujun Shen, Haochen Wang +6
Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and…
SparseTT: Visual Tracking with Sparse Transformers
Zhihong Fu, Zehua Fu, Qingjie Liu +2
Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range de…
Segmentation-Reconstruction-Guided Facial Image De-occlusion
Xiangnan Yin, Di Huang, Zehua Fu +2
Occlusions are very common in face images in the wild, leading to the degraded performance of face-related tasks. Although much effort has been devoted to removing occlusions from…
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast
Ye Du, Zehua Fu, Qingjie Liu +1
Though image-level weakly supervised semantic segmentation (WSSS) has achieved great progress with Class Activation Maps (CAMs) as the cornerstone, the large supervision gap betwee…
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction
Xiangnan Yin, Di Huang, Zehua Fu +2
Although much progress has been made recently in 3D face reconstruction, most previous work has been devoted to predicting accurate and fine-grained 3D shapes. In contrast, relativ…
Pixel Sampling for Style Preserving Face Pose Editing
Xiangnan Yin, Di Huang, Hongyu Yang +3
The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image…
Visual Grounding with Transformers
Ye Du, Zehua Fu, Qingjie Liu +1
In this paper, we propose a transformer based approach for visual grounding. Unlike previous proposal-and-rank frameworks that rely heavily on pretrained object detectors or propos…
STMTrack: Template-free Visual Tracking with Space-time Memory Networks
Zhihong Fu, Qingjie Liu, Zehua Fu +1
Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost tho…
ARM: A Confidence-Based Adversarial Reweighting Module for Coarse Semantic Segmentation
Jingchao Liu, Ye Du, Zehua Fu +2
Coarsely-labeled semantic segmentation annotations are easy to obtain, but therefore bear the risk of losing edge details and introducing background pixels. Impeded by the inherent…