LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding

2025-01-09

AI Summary

To address the challenge of jointly modeling static spatial details, temporal dynamics, and temporal consistency in video-language multimodal understanding tasks (e.g., video content understanding, visual question answering, and story reasoning), this paper proposes an instruction-driven adaptive multi-projector fusion framework. Methodologically, it introduces an instruction-conditioned gating mechanism that dynamically weights and fuses multiple specialized visual projectors—each dedicated to spatial, temporal, or consistency feature encoding—thereby departing from conventional fixed-weight fusion paradigms. The framework is trained via video-language joint fine-tuning coupled with a multi-stage alignment strategy. Extensive experiments demonstrate state-of-the-art performance across multiple video understanding benchmarks, yielding significant improvements in visual question answering and video narrative understanding. The approach achieves new SOTA results while offering enhanced interpretability and adaptability to diverse user instructions.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

In this paper, we introduce LLaVA-Octopus, a novel video multimodal large language model. LLaVA-Octopus adaptively weights features from different visual projectors based on user instructions, enabling us to leverage the complementary strengths of each projector. We observe that different visual projectors exhibit distinct characteristics when handling specific tasks. For instance, some projectors excel at capturing static details, while others are more effective at processing temporal information, and some are better suited for tasks requiring temporal coherence. By dynamically adjusting feature weights according to user instructions, LLaVA-Octopus dynamically selects and combines the most suitable features, significantly enhancing the model's performance in multimodal tasks. Experimental results demonstrate that LLaVA-Octopus achieves excellent performance across multiple benchmarks, especially in tasks such as multimodal understanding, visual question answering, and video understanding, highlighting its broad application potential.

Problem

• Video Understanding
• Multimodal Tasks
• Text-Video Integration

Innovation

• LLaVA-章鱼
• Video-Textual Analysis
• Adaptive Video Content Understanding