VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos

• URL: http://arxiv.org/abs/2411.04923v1
• Date: Thu, 07 Nov 2024 17:59:27 GMT
• Title: VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos
• Authors: Shehan Munasinghe, Hanan Gani, Wenqi Zhu, Jiale Cao, Eric Xing, Fahad Shahbaz Khan, Salman Khan,
• Abstract summary: VideoGLaMM is a new model for fine-grained pixel-level grounding in videos based on user-provided textual inputs. The architecture is trained to synchronize both spatial and temporal elements of video content with textual instructions. Experimental results show that our model consistently outperforms existing approaches across all three tasks.
• Score: 58.765796160750504
• License:
• Abstract: Fine-grained alignment between videos and text is challenging due to complex spatial and temporal dynamics in videos. Existing video-based Large Multimodal Models (LMMs) handle basic conversations but struggle with precise pixel-level grounding in videos. To address this, we introduce VideoGLaMM, a LMM designed for fine-grained pixel-level grounding in videos based on user-provided textual inputs. Our design seamlessly connects three key components: a Large Language Model, a dual vision encoder that emphasizes both spatial and temporal details, and a spatio-temporal decoder for accurate mask generation. This connection is facilitated via tunable V-L and L-V adapters that enable close Vision-Language (VL) alignment. The architecture is trained to synchronize both spatial and temporal elements of video content with textual instructions. To enable fine-grained grounding, we curate a multimodal dataset featuring detailed visually-grounded conversations using a semiautomatic annotation pipeline, resulting in a diverse set of 38k video-QA triplets along with 83k objects and 671k masks. We evaluate VideoGLaMM on three challenging tasks: Grounded Conversation Generation, Visual Grounding, and Referring Video Segmentation. Experimental results show that our model consistently outperforms existing approaches across all three tasks.

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