most citedUncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Fusion Meets Diverse Conditions: A High-diversity Benchmark and Baseline for UAV-based Multimodal Object Detection with Condition Cues

Chen Chen, Kangcheng Bin, Ting Hu +6

Unmanned aerial vehicles (UAV)-based object detection with visible (RGB) and infrared (IR) images facilitates robust around-the-clock detection, driven by advancements in deep lear…

cs.CV2025

A Causal Adjustment Module for Debiasing Scene Graph Generation

Li Liu, Shuzhou Sun, Shuaifeng Zhi +4

While recent debiasing methods for Scene Graph Generation (SGG) have shown impressive performance, these efforts often attribute model bias solely to the long-tail distribution of…

cs.LG20251 cited

Uncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation

Shuzhou Sun, Li Liu, Yongxiang Liu +4

Bias in Foundation Models (FMs) - trained on vast datasets spanning societal and historical knowledge - poses significant challenges for fairness and equity across fields such as h…

cs.CV2025

ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild

Yongxiang Liu, Weijie Li, Li Liu +8

The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application…

eess.IV2024

MaDiNet: Mamba Diffusion Network for SAR Target Detection

Jie Zhou, Chao Xiao, Bowen Peng +4

The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperativ…

cs.CV2024

UEVAVD: A Dataset for Developing UAV's Eye View Active Object Detection

Xinhua Jiang, Tianpeng Liu, Li Liu +2

Occlusion is a longstanding difficulty that challenges the UAV-based object detection. Many works address this problem by adapting the detection model. However, few of them exploit…