7 papers
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…
EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction
Hsi-Che Lin, Yu-Chu Yu, Kai-Po Chang +1
Open-source foundation models have seen rapid adoption and development, enabling powerful general-purpose capabilities across diverse domains. However, fine-tuning large foundation…
SEASON: Mitigating Temporal Hallucination in Video Large Language Models via Self-Diagnostic Contrastive Decoding
Chang-Hsun Wu, Kai-Po Chang, Yu-Yang Sheng +3
Video Large Language Models (VideoLLMs) have shown remarkable progress in video understanding. However, these models still struggle to effectively perceive and exploit rich tempora…
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…
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…
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…