70 citations · 89 across the 13 of their papers we have counts for
13 papers · 1 filter
Semantic-aware SAM for Point-Prompted Instance Segmentation
Zhaoyang Wei, Pengfei Chen, Xuehui Yu +3
Single-point annotation in visual tasks, with the goal of minimizing labelling costs, is becoming increasingly prominent in research. Recently, visual foundation models, such as Se…
Boosting Segment Anything Model Towards Open-Vocabulary Learning
Xumeng Han, Longhui Wei, Xuehui Yu +6
The recent Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model, showcasing potent zero-shot generalization and flexible prompting. Despite SAM fi…
P2RBox: Point Prompt Oriented Object Detection with SAM
Guangming Cao, Xuehui Yu, Wenwen Yu +5
Single-point annotation in oriented object detection of remote sensing scenarios is gaining increasing attention due to its cost-effectiveness. However, due to the granularity ambi…
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Di Wu, Pengfei Chen, Xuehui Yu +3
Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low ann…
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
Pengfei Chen, Xuehui Yu, Xumeng Han +7
Object detection using single point supervision has received increasing attention over the years. However, the performance gap between point supervised object detection (PSOD) and…
Object Localization under Single Coarse Point Supervision
Xuehui Yu, Pengfei Chen, Di Wu +6
Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotatio…
P2P-Loc: Point to Point Tiny Person Localization
Xuehui Yu, Di Wu, Qixiang Ye +2
Bounding-box annotation form has been the most frequently used method for visual object localization tasks. However, bounding-box annotation relies on a large amount of precisely a…
Rethinking Sampling Strategies for Unsupervised Person Re-identification
Xumeng Han, Xuehui Yu, Guorong Li +5
Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that samp…
Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
Nan Jiang, Kuiran Wang, Xiaoke Peng +7
Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce and recreation. With this, monitoring the operation status of UAVs is crucially important. In this work,…
SM+: Refined Scale Match for Tiny Person Detection
Nan Jiang, Xuehui Yu, Xiaoke Peng +2
Detecting tiny objects ( e.g., less than 20 x 20 pixels) in large-scale images is an important yet open problem. Modern CNN-based detectors are challenged by the scale mismatch bet…
Effective Fusion Factor in FPN for Tiny Object Detection
Yuqi Gong, Xuehui Yu, Yao Ding +3
FPN-based detectors have made significant progress in general object detection, e.g., MS COCO and PASCAL VOC. However, these detectors fail in certain application scenarios, e.g.,…
The 1st Tiny Object Detection Challenge:Methods and Results
Xuehui Yu, Zhenjun Han, Yuqi Gong +22
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a…
Scale Match for Tiny Person Detection
Xuehui Yu, Yuqi Gong, Nan Jiang +2
Visual object detection has achieved unprecedented ad-vance with the rise of deep convolutional neural networks.However, detecting tiny objects (for example tiny per-sons less than…