7 citations · 12 across the 24 of their papers we have counts for
29 papers
ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding
Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang +2
In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on offline inference with g…
Scal3R: Learning Efficient Multi-Relative Pose Query for Scalable Online 3D Reconstruction
Chin-Yang Lin, Yang-Che Sun, Cheng Sun +5
Online 3D reconstruction models perform poorly on long videos. This happens because regressing poses relative to a fixed first-frame anchor forces extrapolation far beyond the trai…
PhysCaP: Grounding Code-as-Policy Agent with Physics-Informed Exploration
Chen-Yu Lin, Jing-Wen Chen, Hsueh-En Chang +8
We present PhysCaP, a Physics-Informed Code-as-Policy agent system for active perception in robotic manipulation. While vision-language-action policies excel at imitating demonstra…
Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them
Woojung Han, Seil Kang, Youngjun Jun +3
Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws. We reveal a surprising findi…
CANDLE: Illumination-Invariant Semantic Priors for Color Ambient Lighting Normalization
Rong-Lin Jian, Ting-Yao Chen, Yu-Fan Lin +4
Color ambient lighting normalization under multi-colored illumination is challenging due to severe chromatic shifts, highlight saturation, and material-dependent reflectance. Exist…
NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods
Jie Cai, Kangning Yang, Zhiyuan Li +50
In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academ…
Frequency Switching Mechanism for Parameter-E!cient Multi-Task Learning
Shih-Wen Liu, Yen-Chang Chen, Wei-Ta Chu +2
Multi-task learning (MTL) aims to enable a single model to solve multiple tasks efficiently; however, current parameter-efficient fine-tuning (PEFT) methods remain largely limited…
Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
Chi-Pin Huang, Yunze Man, Zhiding Yu +4
Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show…
TA-Prompting: Enhancing Video Large Language Models for Dense Video Captioning via Temporal Anchors
Wei-Yuan Cheng, Kai-Po Chang, Chi-Pin Huang +2
Dense video captioning aims to interpret and describe all temporally localized events throughout an input video. Recent state-of-the-art methods leverage large language models (LLM…
3AM: 3egment Anything with Adaptive Geometric Consistency in Videos
Yang-Che Sun, Cheng Sun, Chin-Yang Lin +4
Consistent video object tracking must preserve identity under large viewpoint changes, temporary absence, and nearby distractors. Memory-based trackers such as SAM2 rely on appeara…
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…
PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation
Chia-Ming Lee, Ching-Heng Cheng, Yu-Fan Lin +5
Recent advances in All-in-One (AiO) RGB image restoration have demonstrated the effectiveness of prompt learning in handling multiple degradations within a single model. However, e…
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models
Yu-Chu Yu, Chi-Pin Huang, Jr-Jen Chen +4
Large-scale vision-language models (VLMs) have shown a strong zero-shot generalization capability on unseen-domain data. However, adapting pre-trained VLMs to a sequence of downstr…
Language-Guided Transformer for Federated Multi-Label Classification
I-Jieh Liu, Ci-Siang Lin, Fu-En Yang +1
Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private dat…
Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers
Chi-Pin Huang, Kai-Po Chang, Chung-Ting Tsai +3
Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. To perform reliable concept erasu…
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation
Fu-En Yang, Chien-Yi Wang, Yu-Chiang Frank Wang
Federated learning (FL) emerges as a decentralized learning framework which trains models from multiple distributed clients without sharing their data to preserve privacy. Recently…
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators
Yuan-Chia Cheng, Zu-Yun Shiau, Fu-En Yang +1
To understand how deep neural networks perform classification predictions, recent research attention has been focusing on developing techniques to offer desirable explanations. How…
Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and Beyond
Cheng-Yen Hsieh, Chih-Jung Chang, Fu-En Yang +1
While self-supervised learning has been shown to benefit a number of vision tasks, existing techniques mainly focus on image-level manipulation, which may not generalize well to do…