Fast Video Comprehension through Large Language Models with Multimodal Tools

Основні поняття

The core message of this paper is that the key to effectively reasoning about videos using large language models (LLMs) is to focus on the most relevant video events, which can be achieved by leveraging lightweight multimodal tools for structured and descriptive video content extraction, and an efficient Instruction-Oriented Video Events Recognition (InsOVER) algorithm for aligning language instructions with video events.

Анотація

The paper introduces VidCoM, a fast and adaptive framework for video content comprehension that leverages LLMs to reason about videos using lightweight visual tools. The key insights are:

The core challenge in video content comprehension is the difficulty for LLMs to directly interact with video content. VidCoM addresses this by using two visual tools - scene graph generation and image caption generation - to extract structured and descriptive information about relevant video events.

VidCoM employs a two-stage Instruction-Oriented Video Events Recognition (InsOVER) algorithm to automatically initialize video events and then refine them based on language instructions. The first stage uses a moving average-based approach to coarsely identify event boundaries, while the second stage performs a bipartite graph matching between linguistic and visual sub-events to precisely align the events with the instructions.

The LLM agent in VidCoM performs multiple reasoning steps on the extracted video event information to generate the final response, leveraging its world knowledge to complement the visual understanding.

Extensive experiments on Video Question Answering and Dense Video Captioning tasks show that VidCoM outperforms previous state-of-the-art methods, including large-scale video-language models, in both few-shot and supervised settings.

Статистика

The total duration of the video is 30.2817s, and the width and height of each frame are 270.0 and 480.0.

Цитати

"The key to successively achieve responses on long videos is the concentration on the most relevant video events, and the events information can be gathered and represented by two essential visual tools - the scene graph generation tool and the image caption generation tool."
"The proposed InsOVER algorithm bridges the gap between language models and video streams."

Ключові висновки, отримані з

VidCoM: Fast Video Comprehension through Large Language Models with Multimodal Tools

by Ji Qi,Kaixua... о arxiv.org 04-30-2024

https://arxiv.org/pdf/2310.10586.pdf

Глибші Запити

How can the performance of VidCoM be further improved by incorporating more advanced visual understanding tools or by fine-tuning the LLM agent on specific video datasets

To further enhance the performance of VidCoM, several strategies can be implemented. One approach is to incorporate more advanced visual understanding tools to extract richer and more detailed information from the video frames. For example, utilizing state-of-the-art object detection models like YOLO (You Only Look Once) or Faster R-CNN can improve the accuracy of identifying objects and their relationships in the video scenes. By integrating these tools into the framework, VidCoM can generate more comprehensive scene graphs and captions, leading to a better understanding of the video content.
Another way to boost performance is by fine-tuning the Large Language Model (LLM) agent on specific video datasets. By training the LLM on domain-specific video-text data, the model can learn to better comprehend the nuances and context of the videos in that particular domain. Fine-tuning allows the LLM to adapt its language understanding capabilities to the intricacies of the video content, resulting in more accurate responses to user instructions. Additionally, fine-tuning can help the LLM to capture domain-specific knowledge and improve its reasoning abilities in the context of the given videos.

What are the potential limitations or failure cases of the current InsOVER algorithm, and how could it be extended to handle more complex video-language alignment scenarios

The current InsOVER algorithm, while effective in recognizing and aligning language instructions with video events, may have limitations and potential failure cases in handling more complex video-language alignment scenarios. One limitation is the algorithm's reliance on textual assertions extracted from OpenIE models, which may not always capture the full context or semantics of the language instructions. This could lead to mismatches or inaccuracies in aligning the instructions with the video events.
To address these limitations and handle more complex scenarios, the InsOVER algorithm could be extended in the following ways:

Semantic Understanding: Enhance the algorithm to incorporate semantic understanding techniques to better interpret the language instructions and video events. This could involve leveraging pre-trained language models like BERT or RoBERTa to capture the contextual meaning of the instructions and events more accurately.

Multi-Modal Fusion: Integrate multi-modal fusion techniques to combine information from both visual and textual modalities more effectively. By fusing features from scene graphs, image captions, and language instructions, the algorithm can create a more comprehensive representation of the video content for alignment.

Dynamic Matching: Implement dynamic matching strategies that adaptively adjust the matching criteria based on the complexity and variability of the video-language alignment task. This can help the algorithm handle diverse scenarios and improve alignment accuracy in challenging cases.

Feedback Mechanism: Introduce a feedback mechanism that allows the algorithm to learn from its mistakes and refine its alignment over time. By incorporating a feedback loop, the algorithm can iteratively improve its performance and adapt to evolving video-language alignment requirements.

Given the demonstrated effectiveness of VidCoM on video comprehension tasks, how could the framework be adapted or extended to enable other video-centric applications, such as video retrieval, video summarization, or video-guided decision making

The success of VidCoM in video comprehension tasks opens up opportunities for its adaptation and extension to other video-centric applications. Here are some ways the framework could be adapted or extended for different applications:

Video Retrieval: VidCoM can be adapted for video retrieval tasks by incorporating a similarity metric that compares user queries with the extracted video content representations. By leveraging the framework's ability to understand and reason about video content, it can effectively retrieve relevant videos based on user queries or search terms.

Video Summarization: For video summarization, VidCoM can be extended to generate concise and informative summaries of long videos. By identifying key events, objects, and actions in the video and leveraging the LLM's language generation capabilities, the framework can create coherent summaries that capture the essence of the video content.

Video-Guided Decision Making: In the context of video-guided decision making, VidCoM can assist in analyzing videos to provide insights or recommendations for decision-making processes. By integrating domain-specific knowledge and reasoning capabilities, the framework can help in interpreting video data to support decision-making in various fields such as healthcare, security, or autonomous systems.

By adapting and extending VidCoM for these applications, the framework can showcase its versatility and effectiveness in a wide range of video-centric tasks, demonstrating its potential for real-world applications beyond video comprehension.