InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling (2501.12386v2)

Abstract: This paper aims to improve the performance of video multimodal LLMs (MLLM) via long and rich context (LRC) modeling. As a result, we develop a new version of InternVideo2.5 with a focus on enhancing the original MLLMs' ability to perceive fine-grained details and capture long-form temporal structure in videos. Specifically, our approach incorporates dense vision task annotations into MLLMs using direct preference optimization and develops compact spatiotemporal representations through adaptive hierarchical token compression. Experimental results demonstrate this unique design of LRC greatly improves the results of video MLLM in mainstream video understanding benchmarks (short & long), enabling the MLLM to memorize significantly longer video inputs (at least 6x longer than the original), and master specialized vision capabilities like object tracking and segmentation. Our work highlights the importance of multimodal context richness (length and fineness) in empowering MLLM's innate abilites (focus and memory), providing new insights for future research on video MLLM. Code and models are available at https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2.5

• The paper presents long and rich context modeling to improve video MLLMs' understanding of extended temporal dependencies and fine-grained visual details.
• It employs a novel hierarchical token compression technique that enables processing video inputs six times longer with lower computational costs.
• The study leverages task preference optimization to transfer dense vision annotations, enhancing performance in object tracking and segmentation.

Overview of "InternVideo2.5": Empowering Video MLLMs with Long and Rich Context Modeling

The paper "InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling" outlines a novel approach to enhance the capabilities of video multimodal LLMs (MLLMs). The advancement focuses on improving the perception of fine-grained details and capturing long-form temporal structures in video content. The proposed method achieves these improvements by incorporating dense vision task annotations and developing spatiotemporal representations through adaptive hierarchical token compression.

Key Innovations

1. Long and Rich Context (LRC) Modeling:
   • The paper introduces LRC modeling as a principal method to expand the capabilities of video MLLMs. This involves creating models that can process long-term temporal dependencies and provide fine-grained detail analysis, essential for complex narrative understanding and reasoning over video sequences.
2. Hierarchical Token Compression (HiCo):
   • HiCo is employed to enhance context length by compressing multimodal tokens, thus allowing the model to handle video inputs at least six times longer than previous iterations. This technique is crucial for reducing computational overhead while maintaining the integrity of information across video frames.
3. Task Preference Optimization (TPO):
   • By transferring dense vision annotations using direct preference optimization, the model strengthens its ability to tackle vision-specific tasks such as object tracking and segmentation. This optimization involves leveraging state-of-the-art vision expert models to refine the model's understanding and interaction with video content.

Experimental Performance

The paper presents empirical results demonstrating significant improvements across video understanding benchmarks. InternVideo2.5 shows superior performance with the ability to handle extended video sequences and detailed visual perception. It outperforms many state-of-the-art models, particularly in benchmarks requiring long-term memory and focused attention.

Implications

Practical Implications:

• Applications in Video Analysis: The enhanced capacity for long-context processing positions InternVideo2.5 as a valuable tool for various applications, including surveillance analysis, sports analytics, and movie content evaluation.
• Scalable Framework: Through effective memory and computational resource management, the proposed framework can potentially aid in developing systems that require real-time video content interpretation and interactive response generation.

Theoretical Implications:

• Advancements in Multimodal Context Modeling: This work highlights the significance of context richness in both length and granularity for refining the cognitive functions of MLLMs.
• Framework for Future Research: The methodologies proposed, particularly HiCo and TPO, pave the way for further exploration into how these mechanisms can be optimized or augmented with other emerging technologies in video and multimodal analysis.

Speculation on Future AI Developments

Looking ahead, the principles applied in InternVideo2.5 may lead to more integrative AI systems combining vision and language understanding. Future research could explore optimizing MLLMs to achieve more complex interactions in real-world applications, potentially incorporating other sensory data such as audio and haptic feedback. Additionally, extending these models' capabilities to fully autonomous systems remains an area ripe for exploration, fostering advancements in AI-driven content production, robotic perception, and human-computer interaction.

In conclusion, "InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling" signifies a substantial step forward in the development of advanced video understanding frameworks. The paper offers valuable insights into achieving enhanced perceptual and reasoning capabilities in MLLMs, setting a foundation for future explorations into more dynamically interactive AI systems.