LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models (2311.17043v1)

Abstract: In this work, we present a novel method to tackle the token generation challenge in Vision LLMs (VLMs) for video and image understanding, called LLaMA-VID. Current VLMs, while proficient in tasks like image captioning and visual question answering, face computational burdens when processing long videos due to the excessive visual tokens. LLaMA-VID addresses this issue by representing each frame with two distinct tokens, namely context token and content token. The context token encodes the overall image context based on user input, whereas the content token encapsulates visual cues in each frame. This dual-token strategy significantly reduces the overload of long videos while preserving critical information. Generally, LLaMA-VID empowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token. It is proved to surpass previous methods on most of video- or image-based benchmarks. Code is available https://github.com/dvlab-research/LLaMA-VID}{https://github.com/dvlab-research/LLaMA-VID

• The paper introduces a dual-token framework that compresses video frames into a context token and a content token to optimize computational efficiency.
• It leverages transformer-based architectures like ViT and QFormer to integrate visual and textual features, effectively preserving key information.
• Experimental results demonstrate that LLaMA-VID outperforms previous methods on video QA and image benchmarks while maintaining high accuracy.

LLaMA-VID: An Image is Worth 2 Tokens in LLMs

The paper introduces LLaMA-VID, a novel methodology aimed at optimizing token generation within Vision LLMs (VLMs) to enhance video and image comprehension. This approach addresses a significant challenge in current VLM architectures—specifically, the computational burden linked to processing extensive visual tokens in long video sequences. By leveraging a dual-token strategy, LLaMA-VID efficiently condenses video frames into two tokens, significantly enhancing computational efficiency while preserving critical information.

Framework and Methodology

LLaMA-VID innovatively utilizes two types of tokens: a context token and a content token. The context token encapsulates the overall context of an image or video frame, guided by user input, whereas the content token retains detailed visual cues. The distinction between these tokens allows the framework to compress information effectively, supporting the processing of hour-long videos.

For the generation of these tokens, LLaMA-VID integrates a visual encoder and a text decoder, utilizing cutting-edge transformer-based architectures such as ViT and QFormer. The context token is derived using context attention, a mechanism that aggregates text-related visual features, allowing the model to condense broad information efficiently into a single token. This approach ensures that the most pertinent visual cues are maintained, significantly reducing the number of tokens needed for each frame of a prolonged video sequence.

Experimental Results

LLaMA-VID demonstrated its efficacy through extensive empirical evaluations, outperforming preceding methods across numerous video- and image-based benchmarks. In video-based zero-shot QA datasets, such as MSVD-QA and MSRVTT-QA, the proposed method achieved superior performance, showcasing its potential in handling video data with minimal tokens. Notably, this efficiency does not come at the cost of accuracy or visual comprehension, as evidenced by its leading scores in both video summarization and detailed reasoning tasks.

With image-based inputs, LLaMA-VID also shows promise by extending the upper limit of VLMs through the novel utilization of context tokens. The results indicate considerable improvements across a range of visual question answering and understanding benchmarks, highlighting the generality and robustness of the proposed approach.

Implications and Future Work

LLaMA-VID's ability to significantly compress video content into minimal tokens without sacrificing critical information has important implications for the practical deployment of VLMs in real-world applications, such as video analytics and multimedia content understanding. This advancement is crucial for scenarios requiring the efficient processing of extensive datasets, which are common in industrial settings.

Theoretically, LLaMA-VID contributes to the growing body of research on efficient data representation in large-scale AI systems. By demonstrating the feasibility of such a dual-token strategy, it opens avenues for exploring further token optimization techniques and their impact on other domains of AI.

Future developments may explore the dynamic adaptability of token compression levels, allowing models to adjust based on resource availability and task complexity. Additionally, the integration of more nuanced user instructions could further refine the context token's efficacy, enhancing its precision in applications where understanding context-specific cues is imperative.

In summary, LLaMA-VID presents a sophisticated approach to token generation in VLMs, providing meaningful advancements in both computational efficiency and comprehensive understanding of visual content. The strategic design and empirical validation position it as a significant contribution to the field of AI-driven video and image comprehension.