Publications (18)
Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention
Ziming Liu, Song Guo, Jingcai Guo +2
Multi-label zero-shot learning extends conventional single-label zero-shot learning to a more realistic scenario that aims at recognizing multiple unseen labels of classes for each…
Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Models
Kang Wei, Xin Yuan, Fushuo Huo +5
Diffusion models (DMs) have been investigated in various domains due to their ability to generate high-quality data, thereby attracting significant attention. However, similar to t…
Towards Robust Multimodal Learning in the Open World
Fushuo Huo
The rapid evolution of machine learning has propelled neural networks to unprecedented success across diverse domains. In particular, multimodal learning has emerged as a transform…
SpatialBench: Is Your Spatial Foundation Model an All-Round Player?
Haosong Peng, Hao Li, Jiaqi Chen +10
While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing…
On the Evolution of Federated Post-Training Large Language Models: A Model Accessibility View
Tao Guo, Junxiao Wang, Fushuo Huo +4
Federated Learning (FL) enables training models across decentralized data silos while preserving client data privacy. Recent research has explored efficient methods for post-traini…
EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models
Botai Yuan, Yutian Zhou, Yingjie Wang +9
Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety. We study sycophancy -- models' tendency to un…