3 citations · 3 across the 6 of their papers we have counts for
12 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…
LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation
Zhuo Chen, Xinzhe Yuan, Jianshu Zhang +8
The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayesian optimization (BO). Howev…
Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
Xinzhe Yuan, Zhuo Chen, Jianshu Zhang +4
Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for science. Bayesian Optimizat…
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…