VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos

• URL: http://arxiv.org/abs/2506.02448v1
• Date: Tue, 03 Jun 2025 05:12:48 GMT
• Title: VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos
• Authors: Baoyu Liang, Qile Su, Shoutai Zhu, Yuchen Liang, Chao Tong,
• Abstract summary: We propose the task of video event understanding that extracts event scripts and makes predictions with these scripts from videos.<n>To support this task, we introduce VidEvent, a large-scale dataset containing over 23,000 well-labeled events.<n>The dataset was created through a meticulous annotation process, ensuring high-quality and reliable event data.
• Score: 6.442765801124304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the significant impact of visual events on human cognition, understanding events in videos remains a challenging task for AI due to their complex structures, semantic hierarchies, and dynamic evolution. To address this, we propose the task of video event understanding that extracts event scripts and makes predictions with these scripts from videos. To support this task, we introduce VidEvent, a large-scale dataset containing over 23,000 well-labeled events, featuring detailed event structures, broad hierarchies, and logical relations extracted from movie recap videos. The dataset was created through a meticulous annotation process, ensuring high-quality and reliable event data. We also provide comprehensive baseline models offering detailed descriptions of their architecture and performance metrics. These models serve as benchmarks for future research, facilitating comparisons and improvements. Our analysis of VidEvent and the baseline models highlights the dataset's potential to advance video event understanding and encourages the exploration of innovative algorithms and models. The dataset and related resources are publicly available at www.videvent.top.

Related papers

• Grounding Partially-Defined Events in Multimodal Data [61.0063273919745]
  We introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. We propose a benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems.
  arXiv  Detail & Related papers  (2024-10-07T17:59:48Z)
• EA-VTR: Event-Aware Video-Text Retrieval [97.30850809266725]
  Event-Aware Video-Text Retrieval model achieves powerful video-text retrieval ability through superior video event awareness. EA-VTR can efficiently encode frame-level and video-level visual representations simultaneously, enabling detailed event content and complex event temporal cross-modal alignment.
  arXiv  Detail & Related papers  (2024-07-10T09:09:58Z)
• Towards Event-oriented Long Video Understanding [101.48089908037888]
  Event-Bench is an event-oriented long video understanding benchmark built on existing datasets and human annotations. VIM is a cost-effective method that enhances video MLLMs using merged, event-intensive video instructions.
  arXiv  Detail & Related papers  (2024-06-20T09:14:19Z)
• A Survey of Video Datasets for Grounded Event Understanding [34.11140286628736]
  multimodal AI systems must be capable of well-rounded common-sense reasoning akin to human visual understanding. We survey 105 video datasets that require event understanding capability.
  arXiv  Detail & Related papers  (2024-06-14T00:36:55Z)
• Event-aware Video Corpus Moment Retrieval [79.48249428428802]
  Video Corpus Moment Retrieval (VCMR) is a practical video retrieval task focused on identifying a specific moment within a vast corpus of untrimmed videos. Existing methods for VCMR typically rely on frame-aware video retrieval, calculating similarities between the query and video frames to rank videos. We propose EventFormer, a model that explicitly utilizes events within videos as fundamental units for video retrieval.
  arXiv  Detail & Related papers  (2024-02-21T06:55:20Z)
• Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions [75.77044856100349]
  We present the Spoken Moments dataset of 500k spoken captions each attributed to a unique short video depicting a broad range of different events. We show that our AMM approach consistently improves our results and that models trained on our Spoken Moments dataset generalize better than those trained on other video-caption datasets.
  arXiv  Detail & Related papers  (2021-05-10T16:30:46Z)
• Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events [106.19047816743988]
  We present a new large-scale dataset with comprehensive annotations, named Human-in-Events or HiEve. It contains a record number of poses (>1M), the largest number of action instances (>56k) under complex events, as well as one of the largest numbers of trajectories lasting for longer time. Based on its diverse annotation, we present two simple baselines for action recognition and pose estimation.
  arXiv  Detail & Related papers  (2020-05-09T18:24:52Z)

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.