Enhancing Temporal Modeling of Video LLMs via Time Gating

• URL: http://arxiv.org/abs/2410.05714v1
• Date: Tue, 8 Oct 2024 06:21:29 GMT
• Title: Enhancing Temporal Modeling of Video LLMs via Time Gating
• Authors: Zi-Yuan Hu, Yiwu Zhong, Shijia Huang, Michael R. Lyu, Liwei Wang,
• Abstract summary: Video Large Language Models (Video LLMs) have achieved impressive performance on video-and-language tasks, such as video question answering. Most existing Video LLMs neglect temporal information in video data, leading to struggles with temporal-aware video understanding. We propose a Time Gating Video LLM (TG-Vid) designed to enhance temporal modeling through a novel Time Gating module (TG)
• Score: 38.86742466948778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Large Language Models (Video LLMs) have achieved impressive performance on video-and-language tasks, such as video question answering. However, most existing Video LLMs neglect temporal information in video data, leading to struggles with temporal-aware video understanding. To address this gap, we propose a Time Gating Video LLM (TG-Vid) designed to enhance temporal modeling through a novel Time Gating module (TG). The TG module employs a time gating mechanism on its sub-modules, comprising gating spatial attention, gating temporal attention, and gating MLP. This architecture enables our model to achieve a robust understanding of temporal information within videos. Extensive evaluation of temporal-sensitive video benchmarks (i.e., MVBench, TempCompass, and NExT-QA) demonstrates that our TG-Vid model significantly outperforms the existing Video LLMs. Further, comprehensive ablation studies validate that the performance gains are attributed to the designs of our TG module. Our code is available at https://github.com/LaVi-Lab/TG-Vid.

Related papers

• ReVisionLLM: Recursive Vision-Language Model for Temporal Grounding in Hour-Long Videos [25.988212332357545]
  ReVisionLLM is a vision-language model designed to locate events in hour-long videos. Inspired by human search strategies, our model initially targets broad segments of interest. Our model can seamlessly handle videos of vastly different lengths, from minutes to hours.
  arXiv  Detail & Related papers  (2024-11-22T12:46:50Z)
• TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning [42.928144657587325]
  This paper proposes TimeSuite, a collection of new designs to adapt the existing short-form video MLLMs for long video understanding. TimeSuite provides a successful solution to enhance the long video understanding capability of short-form MLLM. In addition, we introduce the TimePro, a comprehensive grounding-centric instruction dataset composed of 9 tasks and 349k high-quality grounded annotations.
  arXiv  Detail & Related papers  (2024-10-25T17:19:55Z)
• VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding [7.907951246007355]
  Video Temporal Grounding (VTG) focuses on accurately identifying event timestamps within a particular video based on a linguistic query. Video Large Language Models (video LLMs) have made significant progress in understanding video content, but they often face challenges in accurately pinpointing timestamps within videos. We propose a specially designed video LLM model for VTG tasks, VTG-LLM, which effectively integrates timestamp knowledge into visual tokens.
  arXiv  Detail & Related papers  (2024-05-22T06:31:42Z)
• MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding [66.56100008577134]
  This study focuses on designing an efficient and effective model for long-term video understanding. We propose to process videos in an online manner and store past video information in a memory bank. Our model can achieve state-of-the-art performances across multiple datasets.
  arXiv  Detail & Related papers  (2024-04-08T17:59:24Z)
• LongVLM: Efficient Long Video Understanding via Large Language Models [55.813206751150716]
  LongVLM is a simple yet powerful VideoLLM for long video understanding. We encode video representations that incorporate both local and global information. Our model produces more precise responses for long video understanding.
  arXiv  Detail & Related papers  (2024-04-04T11:33:29Z)
• 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)
• 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)
• 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)
• 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)
• 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.