Do Language Models Understand Time?

• URL: http://arxiv.org/abs/2412.13845v3
• Date: Mon, 24 Feb 2025 03:37:59 GMT
• Title: Do Language Models Understand Time?
• Authors: Xi Ding, Lei Wang,
• Abstract summary: Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and summarization.<n>This work critically examines the role of LLMs in video processing, with a specific focus on their temporal reasoning capabilities.<n>We analyze challenges posed by existing video datasets, including biases, lack of temporal annotations, and domain-specific limitations that constrain the temporal understanding of LLMs.
• Score: 2.290956583394892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and video summarization. Videos inherently pose unique challenges, combining spatial complexity with temporal dynamics that are absent in static images or textual data. Current approaches to video understanding with LLMs often rely on pretrained video encoders to extract spatiotemporal features and text encoders to capture semantic meaning. These representations are integrated within LLM frameworks, enabling multimodal reasoning across diverse video tasks. However, the critical question persists: Can LLMs truly understand the concept of time, and how effectively can they reason about temporal relationships in videos? This work critically examines the role of LLMs in video processing, with a specific focus on their temporal reasoning capabilities. We identify key limitations in the interaction between LLMs and pretrained encoders, revealing gaps in their ability to model long-term dependencies and abstract temporal concepts such as causality and event progression. Furthermore, we analyze challenges posed by existing video datasets, including biases, lack of temporal annotations, and domain-specific limitations that constrain the temporal understanding of LLMs. To address these gaps, we explore promising future directions, including the co-evolution of LLMs and encoders, the development of enriched datasets with explicit temporal labels, and innovative architectures for integrating spatial, temporal, and semantic reasoning. By addressing these challenges, we aim to advance the temporal comprehension of LLMs, unlocking their full potential in video analysis and beyond. Our paper's GitHub repository can be found at https://github.com/Darcyddx/Video-LLM.

Related papers

• Token-Efficient Long Video Understanding for Multimodal LLMs [101.70681093383365]
  STORM is a novel architecture incorporating a dedicated temporal encoder between the image encoder and the Video-LLMs. We show that STORM achieves state-of-the-art results across various long video understanding benchmarks.
  arXiv  Detail & Related papers  (2025-03-06T06:17:38Z)
• Perceive, Query & Reason: Enhancing Video QA with Question-Guided Temporal Queries [50.47265863322891]
  Video Question Answering (Video QA) is a challenging video understanding task that requires models to comprehend entire videos. Recent advancements in Multimodal Large Language Models (MLLMs) have transformed video QA by leveraging their exceptional commonsense reasoning capabilities. We propose T-Former, a novel temporal modeling method that creates a question-guided temporal bridge between frame-wise visual perception and the reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-12-26T17:53:14Z)
• VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs [27.473258727617477]
  Long video understanding presents unique challenges due to the complexity of reasoning over extended timespans. We propose a framework VideoINSTA, i.e. INformative Spatial-TemporAl Reasoning for long-form video understanding. Our model significantly improves the state-of-the-art on three long video question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-09-30T15:04:14Z)
• ST-LLM: Large Language Models Are Effective Temporal Learners [58.79456373423189]
  Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation. How to effectively encode and understand videos in video-based dialogue systems remains to be solved. We propose ST-LLM, an effective video-LLM baseline with spatial-temporal sequence modeling inside LLM.
  arXiv  Detail & Related papers  (2024-03-30T10:11:26Z)
• LITA: Language Instructed Temporal-Localization Assistant [71.68815100776278]
  We introduce time tokens that encode timestamps relative to the video length to better represent time in videos. We also introduce SlowFast tokens in the architecture to capture temporal information at fine temporal resolution. We show that our emphasis on temporal localization also substantially improves video-based text generation compared to existing Video LLMs.
  arXiv  Detail & Related papers  (2024-03-27T22:50:48Z)
• RelationVLM: Making Large Vision-Language Models Understand Visual Relations [66.70252936043688]
  We present RelationVLM, a large vision-language model capable of comprehending various levels and types of relations whether across multiple images or within a video. Specifically, we devise a multi-stage relation-aware training scheme and a series of corresponding data configuration strategies to bestow RelationVLM with the capabilities of understanding semantic relations.
  arXiv  Detail & Related papers  (2024-03-19T15:01:19Z)
• TempCompass: Do Video LLMs Really Understand Videos? [36.28973015469766]
  Existing benchmarks fail to provide a comprehensive feedback on the temporal perception ability of Video LLMs. We propose the textbfTemp benchmark, which introduces a diversity of high-quality temporal aspects and task formats.
  arXiv  Detail & Related papers  (2024-03-01T12:02:19Z)
• Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge [47.750073410717604]
  We introduce Temporal Grounding Bridge (TGB), a novel framework that bootstraps MLLMs with advanced temporal grounding capabilities. We validate TGB across seven video benchmarks and demonstrate substantial performance improvements compared with prior MLLMs. Our model, initially trained on sequences of four frames, effectively handles sequences up to 16 longer without sacrificing performance.
  arXiv  Detail & Related papers  (2024-02-25T10:27:46Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• Temporal Blind Spots in Large Language Models [20.631107338678234]
  Large language models (LLMs) have recently gained significant attention due to their unparalleled ability to perform various natural language processing tasks. This study investigates the underlying limitations of general-purpose LLMs when deployed for tasks that require a temporal understanding.
  arXiv  Detail & Related papers  (2024-01-22T16:20:14Z)
• DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models (Exemplified as A Video Agent) [73.10899129264375]
  This paper explores DoraemonGPT, a comprehensive and conceptually elegant system driven by LLMs to understand dynamic scenes. Given a video with a question/task, DoraemonGPT begins by converting the input video into a symbolic memory that stores task-related attributes. We extensively evaluate DoraemonGPT's effectiveness on three benchmarks and several in-the-wild scenarios.
  arXiv  Detail & Related papers  (2024-01-16T14:33:09Z)
• Video Understanding with Large Language Models: A Survey [97.29126722004949]
  Given the remarkable capabilities of large language models (LLMs) in language and multimodal tasks, this survey provides a detailed overview of recent advancements in video understanding. The emergent capabilities Vid-LLMs are surprisingly advanced, particularly their ability for open-ended multi-granularity reasoning. This survey presents a comprehensive study of the tasks, datasets, benchmarks, and evaluation methodologies for Vid-LLMs.
  arXiv  Detail & Related papers  (2023-12-29T01:56:17Z)
• Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos [42.32528440002539]
  Temporal Sentence Grounding (TSG) aims to localize moments from videos based on the given natural language queries. Existing works are mainly designed for short videos, failing to handle TSG in long videos. We propose a Grounding-Prompter method, which is capable of conducting TSG in long videos through prompting LLM with multimodal information.
  arXiv  Detail & Related papers  (2023-12-28T16:54:21Z)
• Long Video Understanding with Learnable Retrieval in Video-Language Models [36.793956806567834]
  We introduce a learnable retrieval-based video-language model (R-VLM) for efficient long video understanding. Specifically, given a question (Query) and a long video, our model identifies and selects the most relevant K video chunks. This effectively reduces the number of video tokens, eliminates noise interference, and enhances system performance.
  arXiv  Detail & Related papers  (2023-12-08T09:48:36Z)
• VTimeLLM: Empower LLM to Grasp Video Moments [43.51980030572101]
  Large language models (LLMs) have shown remarkable text understanding capabilities. Video LLMs can only provide a coarse description of the entire video. We propose VTimeLLM, a novel Video LLM for fine-grained video moment understanding.
  arXiv  Detail & Related papers  (2023-11-30T10:49:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.