7 citations · 12 across the 24 of their papers we have counts for
7 papers · 1 filter
VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models
Ying Cheng, Yu-Ho Lin, Min-Hung Chen +2
Video anomaly understanding (VAU) aims to provide detailed interpretation and semantic comprehension of anomalous events within videos, addressing limitations of traditional method…
Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment
Kai-Po Chang, Wei-Yuan Cheng, Chi-Pin Huang +2
Recent advancement in multimodal LLMs (MLLMs) has demonstrated their remarkable capability to generate descriptive captions for input videos. However, these models suffer from fact…
LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
Chin-Yang Lin, Cheng Sun, Fu-En Yang +3
LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansi…
ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning
Chi-Pin Huang, Yueh-Hua Wu, Min-Hung Chen +2
Vision-language-action (VLA) reasoning tasks require agents to interpret multimodal instructions, perform long-horizon planning, and act adaptively in dynamic environments. Existin…
Histopathology Image Report Generation by Vision Language Model with Multimodal In-Context Learning
Shih-Wen Liu, Hsuan-Yu Fan, Wei-Ta Chu +2
Automating medical report generation from histopathology images is a critical challenge requiring effective visual representations and domain-specific knowledge. Inspired by the co…
VideoMage: Multi-Subject and Motion Customization of Text-to-Video Diffusion Models
Chi-Pin Huang, Yen-Siang Wu, Hung-Kai Chung +3
Customized text-to-video generation aims to produce high-quality videos that incorporate user-specified subject identities or motion patterns. However, existing methods mainly focu…
MotionMatcher: Motion Customization of Text-to-Video Diffusion Models via Motion Feature Matching
Yen-Siang Wu, Chi-Pin Huang, Fu-En Yang +1
Text-to-video (T2V) diffusion models have shown promising capabilities in synthesizing realistic videos from input text prompts. However, the input text description alone provides…