3 papers
cs.CV2025
SegDebias: Test-Time Bias Mitigation for ViT-Based CLIP via Segmentation
Fangyu Wu, Yujun Cai
Vision language models such as CLIP have shown remarkable performance in zero shot classification, but remain susceptible to spurious correlations, where irrelevant visual features…
cs.AI2025
ContextNav: Towards Agentic Multimodal In-Context Learning
Honghao Fu, Yuan Ouyang, Kai-Wei Chang +3
Recent advances demonstrate that multimodal large language models (MLLMs) exhibit strong multimodal in-context learning (ICL) capabilities, enabling them to adapt to novel vision-l…
cs.CL2025
LLaVA-NeuMT: Selective Layer-Neuron Modulation for Efficient Multilingual Multimodal Translation
Jingxuan Wei, Caijun Jia, Qi Chen +5
Multimodal Machine Translation (MMT) enhances translation quality by incorporating visual context, helping to resolve textual ambiguities. While existing MMT methods perform well i…