Video Q-Former: Multimodal Large Language Model with Spatio-Temporal Querying Transformer Towards Video Understanding
Keywords: Multimodal Large Language Model, Vision-Language Pretraining, Video Understanding
Abstract: Large language models (LLMs) have made remarkable strides in natural language processing tasks. However, effectively processing and understanding visual information remains a challenge for these models. To address this, multimodal large language models have been proposed, which integrate pre-trained visual encoders with LLMs. Although existing image-based approaches have shown success in aligning visual and textual modalities, extending these advancements to videos is challenging due to the richer visual and temporal information they contain. Current methods, including Video-ChatGPT and Video-LLaMA, have limitations in capturing inter-frame relationships and providing sufficient semantic context. To overcome these challenges, we propose Video Q-Former, a model that adaptively extracts spatiotemporal features from videos with a spatio-temporal querying transformer, enhancing the LLM’s comprehension of visual-language alignment. Extensive experiments demonstrate that our model achieves state-of-the-art performance across various datasets in zero-shot video question answering tasks.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 11192
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