activity
20192026
most citedDistance-IoU Loss: Faster and Better Learning for Bounding Box Regression

961 citations · 1k across the 7 of their papers we have counts for

collaborators

9 papers

cs.CL2026

DEL: Digit Entropy Loss for Numerical Learning of Large Language Models

Zhaohui Zheng, Chenhang He, Shihao Wang +3

Number prediction stands as a fundamental capability of large language models (LLMs) in mathematical problem-solving and code generation. The widely adopted maximum likelihood esti…

cs.CV2023

Zone Evaluation: Revealing Spatial Bias in Object Detection

Zhaohui Zheng, Yuming Chen, Qibin Hou +3

A fundamental limitation of object detectors is that they suffer from "spatial bias", and in particular perform less satisfactorily when detecting objects near image borders. For a…

cs.CV2023★ 7 cited

CrossKD: Cross-Head Knowledge Distillation for Object Detection

Jiabao Wang, Yuming Chen, Zhaohui Zheng +3

Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object de…

cs.CV2023★ 46 cited

Large Selective Kernel Network for Remote Sensing Object Detection

Yuxuan Li, Qibin Hou, Zhaohui Zheng +3

Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge pres…

cs.CV2023

Towards Spatial Equilibrium Object Detection

Zhaohui Zheng, Yuming Chen, Qibin Hou +2

Semantic objects are unevenly distributed over images. In this paper, we study the spatial disequilibrium problem of modern object detectors and propose to quantify this ``spatial…

cs.CV2022★ 14 cited

Localization Distillation for Object Detection

Zhaohui Zheng, Rongguang Ye, Qibin Hou +4

Previous knowledge distillation (KD) methods for object detection mostly focus on feature imitation instead of mimicking the prediction logits due to its inefficiency in distilling…