5 papers · 1 filter
Distill What the Student Can See: Fisher-Projected On-Policy Distillation for Vision-Language Models
Leyan Xue, Feng Xiong, Mingjun Ma +1
On-policy distillation (OPD) samples trajectories from the current student policy and minimizes token-level divergence between student and teacher next-token distributions at prefi…
MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains
Leyan Xue, Changqing Zhang, Kecheng Xue +3
Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Current fusion methods are typically e…
Out-Of-Distribution Detection with Diversification (Provably)
Haiyun Yao, Zongbo Han, Huazhu Fu +3
Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine learning models. Recent advancements focus on utilizing easily accessible auxiliary outli…
DOTA: Distributional Test-Time Adaptation of Vision-Language Models
Zongbo Han, Jialong Yang, Guangyu Wang +4
Vision-language foundation models (VLMs), such as CLIP, exhibit remarkable performance across a wide range of tasks. However, deploying these models can be unreliable when signific…
Selective Learning: Towards Robust Calibration with Dynamic Regularization
Zongbo Han, Yifeng Yang, Changqing Zhang +3
Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. This problem usually arises due to the overfitting problem, which…