Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models
HICom uses hybrid-level instruction injection for video token compression, achieving 2.43% performance gain and 78.8% token reduction.
Key Findings
Methodology
HICom employs a hybrid-level instruction injection strategy to guide video token compression. At the local level, instructions are injected into grouped visual tokens, while at the global level, learnable tokens are used. The attention mechanism completes the conditional compression, preserving instruction-relevant visual information and maintaining temporal-spatial structure for better understanding by large language models.
Key Results
- HICom achieved a 2.43% average performance improvement on three multiple-choice QA benchmarks, saving 78.8% tokens compared to state-of-the-art methods.
- Conditional pre-training on the HICom-248K dataset further enhanced model performance, particularly in long video tasks.
- Through hybrid-level instruction injection, HICom excels in video understanding tasks, significantly reducing computational burden.
Significance
HICom introduces a novel strategy for conditional compression in multimodal large language models, addressing the information loss problem inherent in traditional unconditional compression methods. This approach not only improves video understanding accuracy but also significantly reduces computational costs, offering new possibilities for applying multimodal large language models to video tasks.
Technical Contribution
HICom distinguishes itself from existing unconditional compression methods by introducing local and global instruction injection strategies for conditional compression. This approach provides new theoretical insights into compression strategies and demonstrates engineering possibilities for reducing computational burden.
Novelty
HICom is the first to introduce a hybrid-level instruction injection strategy for video token compression, differing from traditional unconditional methods by retaining more instruction-relevant information.
Limitations
- HICom's performance improvement is less pronounced in short video tasks, possibly due to less redundancy in short videos.
- The flexibility of instruction injection could be improved, especially in adapting to different task scenarios.
Future Work
Future work could explore the application of HICom in other multimodal tasks, such as image captioning and audio analysis, and investigate ways to enhance the flexibility and adaptability of instruction injection.
AI Executive Summary
Multimodal large language models (MLLMs) face significant computational burdens when processing video tasks, as traditional compression strategies like average pooling lead to information loss. To address this, the paper proposes a novel hybrid-level instruction injection strategy (HICom) that guides video token compression at both local and global levels. Experimental results show that HICom achieves significant performance improvements on multiple video understanding benchmarks, with an average increase of 2.43% and a 78.8% reduction in tokens.
HICom injects instructions into grouped visual tokens at the local level and learnable tokens at the global level, using the attention mechanism to complete conditional compression. This method retains instruction-relevant visual information while maintaining temporal-spatial structure, facilitating better understanding by large language models. To further unleash HICom's potential, the researchers introduced a new conditional pre-training stage and constructed the HICom-248K dataset.
While HICom excels in long video tasks, its performance improvement in short video tasks is less pronounced. Future research could explore HICom's application in other multimodal tasks and enhance its adaptability across different task scenarios.
Deep Analysis
Background
Multimodal large language models have made significant progress in image understanding tasks, but face challenges in video tasks due to the large number of video frames, leading to high computational burdens. Traditional compression strategies like average pooling result in information loss, failing to effectively retain user instruction-relevant information.
Core Problem
Existing unconditional compression methods in video tasks often lead to the loss of instruction-relevant information, especially in long videos. The challenge is to reduce computational burden while retaining more instruction-relevant information.
Innovation
HICom introduces a hybrid-level instruction injection strategy to guide video token compression at both local and global levels. At the local level, instructions are injected into grouped visual tokens, while at the global level, learnable tokens are used, utilizing the attention mechanism for conditional compression.
Methodology
- �� At the local level, video frame features are grouped, and instruction conditions are injected.
- �� At the global level, a small number of learnable tokens are injected, and attention is calculated.
- �� Conditional pre-training on the HICom-248K dataset enhances model performance.
Experiments
Experiments used the HICom-248K dataset for conditional pre-training and tested on multiple video understanding benchmarks. Compared to state-of-the-art methods, HICom achieved significant performance improvements, especially in long video tasks.
Results
HICom achieved a 2.43% average performance improvement on three multiple-choice QA benchmarks, saving 78.8% tokens compared to state-of-the-art methods. Conditional pre-training further enhanced model performance.
Applications
HICom can be applied to video understanding tasks in multimodal large language models, particularly in scenarios requiring efficient compression and retention of instruction-relevant information, such as video QA and video description.
Limitations & Outlook
HICom's performance improvement is less pronounced in short video tasks, possibly due to less redundancy in short videos. Future work could explore enhancing the flexibility and adaptability of instruction injection.
Plain Language Accessible to non-experts
Imagine you're watching a movie with many scenes, but you're only interested in certain specific scenes. HICom acts like a smart assistant that helps you filter out these specific scenes without you having to search for them yourself. It decides which scenes to keep and which to ignore by injecting your interests into each scene. This way, you can focus more on what you really care about without being distracted by unrelated information.
ELI14 Explained like you're 14
Imagine you're playing a video game where you need to find key items in the game. HICom is like your game assistant, helping you quickly locate these items without having to search the entire game world. It decides which areas to focus on and which to skip by injecting your mission instructions into the game. This way, you can complete tasks faster and score higher!
Glossary
Multimodal Large Language Models (MLLMs)
Models that integrate visual and language information to handle multiple modalities of data.
In video understanding tasks, MLLMs improve comprehension by integrating video and text information.
Instruction Injection
Injecting user instructions as conditions into the model to guide information processing and compression.
In HICom, instruction injection guides video token compression to retain more relevant information.
Conditional Compression
Selective data compression based on specific conditions to retain important information.
HICom uses conditional compression to reduce computational burden while retaining instruction-relevant information.
Attention Mechanism
A mechanism used to highlight important information, commonly used in deep learning models.
In HICom, the attention mechanism is used for conditional compression at local and global levels.
HICom-248K Dataset
A dataset containing 248K video clips used for conditional pre-training in HICom.
This dataset is used to train the instruction injection module in HICom to enhance model performance.
Open Questions Unanswered questions from this research
- 1 How to improve HICom's performance in short video tasks? Current methods perform poorly with less redundancy.
- 2 How to enhance the flexibility and adaptability of instruction injection for different task scenarios?
Applications
Immediate Applications
Video QA
HICom can be used in video QA tasks, improving accuracy through conditional compression and reducing computational burden.
Long-term Vision
Multimodal Intelligent Assistant
HICom has the potential to be a core technology in multimodal intelligent assistants, helping users efficiently filter and process multimodal information.
Abstract
Recent Multi-modal Large Language Models (MLLMs) have been challenged by the computational overhead resulting from massive video frames, often alleviated through compression strategies. However, the visual content is not equally contributed to user instructions, existing strategies (\eg, average pool) inevitably lead to the loss of potentially useful information. To tackle this, we propose the Hybrid-level Instruction Injection Strategy for Conditional Token Compression in MLLMs (HICom), utilizing the instruction as a condition to guide the compression from both local and global levels. This encourages the compression to retain the maximum amount of user-focused information while reducing visual tokens to minimize computational burden. Specifically, the instruction condition is injected into the grouped visual tokens at the local level and the learnable tokens at the global level, and we conduct the attention mechanism to complete the conditional compression. From the hybrid-level compression, the instruction-relevant visual parts are highlighted while the temporal-spatial structure is also preserved for easier understanding of LLMs. To further unleash the potential of HICom, we introduce a new conditional pre-training stage with our proposed dataset HICom-248K. Experiments show that our HICom can obtain distinguished video understanding ability with fewer tokens, increasing the performance by 2.43\% average on three multiple-choice QA benchmarks and saving 78.8\% tokens compared with the SOTA method. The code is available at https://github.com/lntzm/HICom.