562 citations · 2.4k across the 31 of their papers we have counts for
8 papers · 1 filter
Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft
Hao Li, Xue Yang, Zhaokai Wang +7
Many reinforcement learning environments (e.g., Minecraft) provide only sparse rewards that indicate task completion or failure with binary values. The challenge in exploration eff…
PointOBB: Learning Oriented Object Detection via Single Point Supervision
Junwei Luo, Xue Yang, Yi Yu +3
Single point-supervised object detection is gaining attention due to its cost-effectiveness. However, existing approaches focus on generating horizontal bounding boxes (HBBs) while…
Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision
Yi Yu, Xue Yang, Qingyun Li +4
With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning rotated box (RBox) from the horizontal bo…
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…
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
Yan Li, Weiwei Guo, Xue Yang +4
An increasingly massive number of remote-sensing images spurs the development of extensible object detectors that can detect objects beyond training categories without costly colle…
H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection
Yi Yu, Xue Yang, Qingyun Li +4
With the rapidly increasing demand for oriented object detection, e.g. in autonomous driving and remote sensing, the recently proposed paradigm involving weakly-supervised detector…
ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection
Ying Zeng, Yushi Chen, Xue Yang +2
Existing oriented object detection methods commonly use metric AP to measure the performance of the model. We argue that AP is inherently unsuitable for oriented obje…
PatchDCT: Patch Refinement for High Quality Instance Segmentation
Qinrou Wen, Jirui Yang, Xue Yang +1
High-quality instance segmentation has shown emerging importance in computer vision. Without any refinement, DCT-Mask directly generates high-resolution masks by compressed vectors…