11 papers
CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection
Lin Zhu, Yifeng Yang, Qinying Gu +3
Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often involve further fine-tuning of VL-PT…
: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization
Lin Zhu, Yifeng Yang, Xinbing Wang +2
Recent approaches for vision-language models (VLMs) have shown remarkable success in achieving fast downstream adaptation. When applied to real-world downstream tasks, VLMs inevita…
Proximal Action Replacement for Behavior Cloning Actor-Critic in Offline Reinforcement Learning
Jinzong Dong, Wei Huang, Jianshu Zhang +5
Offline reinforcement learning (RL), which optimizes policies using a previously collected static dataset, is an important branch of RL. A popular and promising approach is to regu…
Logit-Attention Divergence: Mitigating Position Bias in Multi-Image Retrieval via Attention-Guided Calibration
Mingtao Xian, Yifeng Yang, Qinying Gu +2
Multimodal Large Language Models (MLLMs) have shown strong performance in multi-image cross-modal retrieval, yet suffer from severe position bias, where predictions are dominated b…
TINS: Test-time ID-prototype-separated Negative Semantics Learning for OOD Detection
Yifeng Yang, Jubo Feng, Jing Xu +3
Vision-language models enable OOD detection by comparing image alignment with ID labels and negative semantics. Existing negative-label-based methods mainly rely on static negative…
Learning Primitive Embodied World Models: Towards Scalable Robotic Learning
Qiao Sun, Liujia Yang, Wei Tang +12
While video-generation-based embodied world models have gained increasing attention, their reliance on large-scale embodied interaction data remains a key bottleneck. The scarcity,…