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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

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7 papers · 1 filter

cs.CV2025

VideoAnchor: Reinforcing Subspace-Structured Visual Cues for Coherent Visual-Spatial Reasoning

Zhaozhi Wang, Tong Zhang, Mingyue Guo +2

Multimodal Large Language Models (MLLMs) have achieved impressive progress in vision-language alignment, yet they remain limited in visual-spatial reasoning. We first identify that…

cs.CV2025

DS-Det: Single-Query Paradigm and Attention Disentangled Learning for Flexible Object Detection

Guiping Cao, Xiangyuan Lan, Wenjian Huang +3

Popular transformer detectors have achieved promising performance through query-based learning using attention mechanisms. However, the roles of existing decoder query types (e.g.,…

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…