Author

Zihang Lin

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#artificial intelligence May 2026

SDGBiasBench: Benchmarking and Mitigating Vision-Language Models' Biases in Sustainable Development Goals

This work proposes CADE (Contrastive Adaptive Debias Ensemble), a training-free, plug-and-play method that leverages modality-specific answer priors that yields significant gains on the proposed benchmark, which can foster the development of more fair and reliable AI systems for sustainable development.

Zihang Lin, Huaiyuan Qin, Mu Yang et al. · arXiv.org · 0 citations

Preprint Aug 2026

DiD It in 87 Minutes: A Label-Free Softmax-to-Linear Adaptation of Vision Transformers for Object Detection

DiD is introduced, a label-free conversion method that exclusively trains the linear-attention backbone by aligning detector-facing interface tensors with those of a frozen Softmax teacher, and substantially outperforms established baselines and matches supervised, fully trained linear models.

Huai-Yuan Qin, Gabriel James Goenawan, Zihang Lin et al. · 1 citation