activity
20232025
most citedBenign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features

9 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection

Guiping Cao, Wenjian Huang, Xiangyuan Lan +3

Small Object Detection (SOD) poses significant challenges due to limited information and the model's low class prediction score. While Transformer-based detectors have shown promis…

cs.CV2025

Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning

Jinpeng Wang, Tianci Luo, Yaohua Zha +7

Visual In-Context Learning (VICL) enables adaptively solving vision tasks by leveraging pixel demonstrations, mimicking human-like task completion through analogy. Prompt selection…

cs.CV2025

RadioFormer: A Multiple-Granularity Radio Map Estimation Transformer with 1\textpertenthousand Spatial Sampling

Zheng Fang, Kangjun Liu, Ke Chen +4

The task of radio map estimation aims to generate a dense representation of electromagnetic spectrum quantities, such as the received signal strength at each grid point within a ge…

cs.CV2025

AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing

Niu Lian, Jun Li, Jinpeng Wang +4

Self-Supervised Video Hashing (SSVH) compresses videos into hash codes for efficient indexing and retrieval using unlabeled training videos. Existing approaches rely on random fram…

cs.CV2024

Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition

Lan Chen, Dong Li, Xiao Wang +5

Existing event stream-based pattern recognition models usually represent the event stream as the point cloud, voxel, image, etc., and design various deep neural networks to learn t…

cs.LG20239 cited

Benign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features

Yi Zhang, Jitao Sang, Junyang Wang +2

Machine learning models often learn to make predictions that rely on sensitive social attributes like gender and race, which poses significant fairness risks, especially in societa…