VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos (2411.04923v1)

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 LLM, 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.

• The paper introduces VideoGLaMM, a Large Multimodal Model with a dual vision encoder and spatio-temporal decoder designed for precise pixel-level visual grounding in video data.
• VideoGLaMM utilizes a large-scale multimodal dataset containing 38,000 video-QA triplets and 671,000 masks to achieve fine-grained spatio-temporal alignment.
• Evaluations show VideoGLaMM outperforms state-of-the-art methods on benchmarks like Grounded Conversation Generation and Referring Video Segmentation, demonstrating superior accuracy.

The paper presents VideoGLaMM, a Large Multimodal Model (LMM) engineered specifically for fine-grained pixel-level visual grounding in video data. The primary goal of VideoGLaMM is to bridge the gap observed in traditional video-based LMMs, which often falter in pixel-level accuracy due to inherent spatial and temporal dynamics.

Architecture Overview:

1. Components:
   • LLM: Facilitates semantic understanding and response generation.
   • Dual Vision Encoder: Separately emphasizes spatial and temporal aspects of videos.
   • Spatio-Temporal Decoder: Generates accurate visual masks for specified objects.
   • Adapters: Tunable Vision-to-Language (V→L) and Language-to-Vision (L→V) adapters ensure close vision-language alignment.
2. Dataset:
   • The model is trained using a large-scale multimodal dataset curated with a semi-automatic annotation pipeline, comprising 38,000 video-QA triplets, 83,000 objects, and 671,000 masks.

Functionality:

• Vision-Language Alignment: Achieved through a sophisticated architecture that integrates spatial and temporal video features closely with linguistic inputs.
• Pixel-Level Mask Generation: The spatio-temporal decoder uses the LLM's textual instructions intertwined with object mask outputs to ensure precise pixel-level grounding.
• Multimodal Dataset: The dataset supports spatio-temporal synchronization of model outputs with video content.

Key Results:

1. Performance Metrics:
   • Evaluated on tasks like Grounded Conversation Generation, Visual Grounding, and Referring Video Segmentation, VideoGLaMM outperforms existing state-of-the-art methods across these benchmarks.
2. Experiments and Evaluations:
   • Demonstrates superior semantic understanding and mask accuracy on complex video datasets.
   • Outshines alternatives such as PG-Video-LLaVA and GLaMM in contextually enriching video-LMM models.
3. Technical Contributions:
   • Introduces a comprehensive and finely-tuned benchmark dataset for robust model evaluation.
   • Provides a refined pipeline for generating highly detailed and contextually accurate video annotations.

Ablation Studies and Architectural Insights:

• Spatio-Temporal Processing: The dual encoder structure is crucial for maintaining a balance between local (spatial) and global (temporal) information, improving model precision.
• Decoder Configuration: A spatio-temporal decoder using eight input frames effectively balances mask accuracy and conversational output quality.
• Integration & End-to-End Training: Finetunes LoRA parameters for the LLM along with the newly proposed adapters, enhancing the model’s decomposition of video scenes into finer detail.

Limitations and Future Directions:

• The potential for annotation noise in the dataset may slightly affect grounding accuracy.
• Extending capabilities to longer video sequences and refining granularity comprehension are suggested future paths.

In summary, VideoGLaMM effectively expands upon current LMM frameworks by incorporating a well-structured spatio-temporal understanding of video content, enabling detailed and contextually-informed pixel-level grounding and interaction.