PVChat: Personalized Video Chat with One-Shot Learning

• URL: http://arxiv.org/abs/2503.17069v1
• Date: Fri, 21 Mar 2025 11:50:06 GMT
• Title: PVChat: Personalized Video Chat with One-Shot Learning
• Authors: Yufei Shi, Weilong Yan, Gang Xu, Yumeng Li, Yuchen Li, Zhenxi Li, Fei Richard Yu, Ming Li, Si Yong Yeo,
• Abstract summary: PVChat is a one-shot learning framework that enables subject-aware question answering from a single video for each subject.<n>Our approach optimize a Mixture-of-Heads (MoH) enhanced ViLLM on a synthetically augmented video-QA dataset.<n>We evaluate PVChat on diverse datasets covering medical scenarios, TV series, anime, and real-world footage.
• Score: 15.328085576102106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video large language models (ViLLMs) excel in general video understanding, e.g., recognizing activities like talking and eating, but struggle with identity-aware comprehension, such as "Wilson is receiving chemotherapy" or "Tom is discussing with Sarah", limiting their applicability in smart healthcare and smart home environments. To address this limitation, we propose a one-shot learning framework PVChat, the first personalized ViLLM that enables subject-aware question answering (QA) from a single video for each subject. Our approach optimizes a Mixture-of-Heads (MoH) enhanced ViLLM on a synthetically augmented video-QA dataset, leveraging a progressive image-to-video learning strategy. Specifically, we introduce an automated augmentation pipeline that synthesizes identity-preserving positive samples and retrieves hard negatives from existing video corpora, generating a diverse training dataset with four QA types: existence, appearance, action, and location inquiries. To enhance subject-specific learning, we propose a ReLU Routing MoH attention mechanism, alongside two novel objectives: (1) Smooth Proximity Regularization for progressive learning through exponential distance scaling and (2) Head Activation Enhancement for balanced attention routing. Finally, we adopt a two-stage training strategy, transitioning from image pre-training to video fine-tuning, enabling a gradual learning process from static attributes to dynamic representations. We evaluate PVChat on diverse datasets covering medical scenarios, TV series, anime, and real-world footage, demonstrating its superiority in personalized feature understanding after learning from a single video, compared to state-of-the-art ViLLMs.

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