1 citations · 2 across the 5 of their papers we have counts for
6 papers
When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models
Hung-Jen Chen, Yu-Hsun Hou, Yan-Hong Chen +4
Vision-language-action (VLA) models can exceed 90% success on in-distribution tasks and withstand nuisance changes that preserve the required action, yet fail under counterfactual…
Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs
Hung-Jen Chen, Yu-Heng Ho, Ting-Yao Huang +4
Generative vision-language models (VLMs) such as Qwen-VL and LLaVA achieve strong zero-shot performance on tasks overlapping with their pretraining distribution, yet fail on specia…
Grounding-Aware Token Pruning: Recovering from Drastic Performance Drops in Visual Grounding Caused by Pruning
Tzu-Chun Chien, Chieh-Kai Lin, Shiang-Feng Tsai +3
Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance in visual grounding, establishing themselves as a general interface for various vision-language…
Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
Pei-Chi Chen, Yi Yao, Chan-Feng Hsu +4
Camouflaged image generation is emerging as a solution to data scarcity in camouflaged vision perception, offering a cost-effective alternative to data collection and labeling. Rec…
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning
Hongxia Xie, Chu-Jun Peng, Yu-Wen Tseng +4
Visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions. This paradigm shows promis…
Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition
Hongxia Xie, Ming-Xian Lee, Tzu-Jui Chen +4
Group affect refers to the subjective emotion that is evoked by an external stimulus in a group, which is an important factor that shapes group behavior and outcomes. Recognizing g…