Video Understanding with Large Language Models: A Survey
(2312.17432)Abstract
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly. Given the remarkable capabilities of LLMs in language and multimodal tasks, this survey provides a detailed overview of the recent advancements in video understanding harnessing the power of LLMs (Vid-LLMs). The emergent capabilities of Vid-LLMs are surprisingly advanced, particularly their ability for open-ended spatial-temporal reasoning combined with commonsense knowledge, suggesting a promising path for future video understanding. We examine the unique characteristics and capabilities of Vid-LLMs, categorizing the approaches into four main types: LLM-based Video Agents, Vid-LLMs Pretraining, Vid-LLMs Instruction Tuning, and Hybrid Methods. Furthermore, this survey presents a comprehensive study of the tasks, datasets, and evaluation methodologies for Vid-LLMs. Additionally, it explores the expansive applications of Vid-LLMs across various domains, highlighting their remarkable scalability and versatility in real-world video understanding challenges. Finally, it summarizes the limitations of existing Vid-LLMs and outlines directions for future research. For more information, readers are recommended to visit the repository at https://github.com/yunlong10/Awesome-LLMs-for-Video-Understanding.
Overview
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LLMs have merged with video content analysis to form Vid-LLMs, broadening the scope of traditional video understanding by incorporating spatial-temporal contexts and knowledge.
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Vid-LLMs extend from a history of video analysis methods, utilizing self-supervised pretraining and language model contexts, categorized into four types: video agents, pretraining methods, instruction tuning, and hybrid approaches.
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In Vid-LLMs, language plays a key role in encoding/decoding and adapters translate different modality inputs into a common domain, essential for combining LLMs with video data.
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Vid-LLMs demonstrate their usefulness in tasks such as video captioning and action recognition, moving beyond simple categorization to intricate understanding and generation.
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Despite their progress and widespread applications, Vid-LLMs face challenges including fine-grained understanding and avoiding content hallucination, with ongoing efforts to enhance these models.
Introduction
LLMs have secured a prominent place in AI advancements, and their convergence with video content has birthed a new interdisciplinary field that combines language and imagery for comprehensive video understanding. This comes at a pivotal time when online video content has burgeoned into the dominant form of media consumption, pushing the boundaries of traditional analysis technologies. The essence of LLMs in video analysis—Video LLMs, or Vid-LLMs—lies in their ability to imbibe spatial-temporal contexts and infer knowledge, propelling strides in video understanding tasks.
Foundations and Taxonomy
Vid-LLMs have emerged out of the rich history of video understanding, transcending conventional methods and neural network models to exploit self-supervised pretraining, and now, most recently, integrating the broad contextual understanding offered by LLMs into video analysis. Vid-LLMs are being constantly improved and can be structurally categorized mainly into four types: LLM-based video agents, pretraining methods, instruction tuning, and hybrid approaches.
The Role of Language and Adapters in Video Understanding
Language, being the bedrock of LLMs, plays a dual role—encoding and decoding. Adapters are pivotal in marrying video modality with language models, where their task is to translate inputs from different modalities into a common language domain. These adapters can range from simple projection layers to complex cross-attention mechanisms, making them crucial for an efficient marriage between LLMs and video content.
Vid-LLMs: Models in Action
Recent implementations of Vid-LLMs showcase their utility in various tasks such as video captioning, action recognition, and more. These models leverage a combination of visual encoders and adapters, orchestrating not just the synthesis of detailed text descriptions but also responding to intricate questions regarding video content. This indicates a major shift from classical methods, which focused narrowly on categorizing video into predefined labels, towards more versatile approaches capable of processing hundreds of frames for nuanced generation and contextual comprehension.
Evaluating Performance and Applications
Several tasks form the crux of video understanding, such as recognition, captioning, grounding, retrieval, and question answering. A wide spectrum of datasets caters to these tasks, ranging from user-generated content to finely annotated movie descriptions. Evaluation metrics, essential for assessing Vid-LLMs, are borrowed from both the computer vision and NLP domains, encompassing metrics like accuracy, BLEU, METEOR, and others.
Future Trajectories and Current Limitations
Despite remarkable progress, challenges remain. Fine-grained understanding, handling long video durations, and ensuring model responses genuinely reflect video content without hallucination are pressing issues. Applications of advanced Vid-LLMs span across various domains from media and entertainment to healthcare and security, highlighting their transformative potential across industries. As research propels forward, addressing limitations such as hallucination and enhancing multi-modal integration are identified as fertile ground for growing the capabilities and applications of Vid-LLMs.
In summary, Vid-LLMs stand at the cusp of revolutionizing video understanding, taking large strides in task-solving capabilities to address the deluge of video content burgeoning in today's digital age. They hold the promise of transforming video analysis, from a labor-intensive manual process to a sophisticated, elegant orchestration of artificial intelligence technology.
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