One Identity, Many Roles: Multimodal Entity Coreference for Enhanced Video Situation Recognition

• URL: http://arxiv.org/abs/2604.23173v1
• Date: Sat, 25 Apr 2026 06:55:13 GMT
• Title: One Identity, Many Roles: Multimodal Entity Coreference for Enhanced Video Situation Recognition
• Abstract summary: Video Situation Recognition (VidSitu) addresses the challenging problem of "who did what to whom, with what how, and where" in video.<n>It tests thorough video understanding by identification of salient actions and associated short descriptions for event roles across multiple events.<n>We propose Multimodal Entity Coreference (MEC) to unite descriptions in text with grounding across the video.
• Score: 17.329901171579973
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Video Situation Recognition (VidSitu) addresses the challenging problem of "who did what to whom, with what, how, and where" in a video. It tests thorough video understanding by requiring identification of salient actions and associated short descriptions for event roles across multiple events. Grounding with VidSitu requires spatio-temporal localization of key entities across shots and varied appearances. We posit that coherent video understanding requires consistent identification of entities that play different roles. We propose Multimodal Entity Coreference (MEC) to unite entity descriptions in text with grounding across the video. Towards this, we introduce CineMEC, a multi-stage approach that unites event role mention groups with visual clusters of entities, without explicit grounding supervision during training. Our approach is designed to exploit the synergy between visual grounding and captioning, where improving one influences the other and vice versa. For evaluation, we extend the VidSitu dataset with grounding annotations. While previous work focuses primarily on descriptions, CineMEC improves consistency across both: captioning (+2.5% CIDEr, +7% LEA) and visual grounding (+18% HOTA).

Related papers

• Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment [20.811984469709508]
  Aura is a unified framework for high-fidelity and identity-consistent video generation.<n>We introduce AI director-level captions that provide dense and structured descriptions of video content.<n>We build a high-quality video-subject image dataset through a dedicated data construction pipeline.
  arXiv  Detail & Related papers  (2026-07-05T13:54:33Z)
• VideoPerceiver: Enhancing Fine-Grained Temporal Perception in Video Multimodal Large Language Models [9.896951371033229]
  VideoPerceiver is a video multimodal large language model (VMLLM) that enhances fine-grained perception in video understanding.<n>We construct "key-information-missing" videos by extracting event-action keywords from captions, identifying corresponding key frames, and replacing them with adjacent frames.<n> Experiments show VideoPerceiver substantially outperforms state-of-the-art VMLLMs on fine-grained action understanding and rare event captioning benchmarks.
  arXiv  Detail & Related papers  (2025-11-24T06:57:26Z)
• VrdONE: One-stage Video Visual Relation Detection [30.983521962897477]
  Video Visual Relation Detection (VidVRD) focuses on understanding how entities over time and space in videos. Traditional methods for VidVRD, challenged by its complexity, typically split the task into two parts: one for identifying what relation are present and another for determining their temporal boundaries. We propose VrdONE, a streamlined yet efficacious one-stage model for VidVRD.
  arXiv  Detail & Related papers  (2024-08-18T08:38:20Z)
• MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions [69.9122231800796]
  We present MMTrail, a large-scale multi-modality video-language dataset incorporating more than 20M trailer clips with visual captions.<n>We propose a systemic captioning framework, achieving various modality annotations with more than 27.1k hours of trailer videos.<n>Our dataset potentially paves the path for fine-grained large multimodal-language model training.
  arXiv  Detail & Related papers  (2024-07-30T16:43:24Z)
• HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model [9.762722976833581]
  Current models rely extensively on instance-level alignment between video and language modalities. We take an inspiration from human perception and explore a compositional approach for ego video representation.
  arXiv  Detail & Related papers  (2024-06-01T05:41:12Z)
• Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation [74.51546366251753]
  Video topic segmentation unveils the coarse-grained semantic structure underlying videos. We introduce a multi-modal video topic segmenter that utilizes both video transcripts and frames. Our proposed solution significantly surpasses baseline methods in terms of both accuracy and transferability.
  arXiv  Detail & Related papers  (2023-11-30T21:59:05Z)
• Exploiting Auxiliary Caption for Video Grounding [66.77519356911051]
  Video grounding aims to locate a moment of interest matching a given query sentence from an untrimmed video. Previous works ignore the sparsity dilemma in video annotations, which fails to provide the context information between potential events and query sentences in the dataset. We propose an Auxiliary Caption Network (ACNet) for video grounding. Specifically, we first introduce dense video captioning to generate dense captions and then obtain auxiliary captions by Non-Auxiliary Caption Suppression (NACS) To capture the potential information in auxiliary captions, we propose Caption Guided Attention (CGA) project the semantic relations between auxiliary captions and
  arXiv  Detail & Related papers  (2023-01-15T02:04:02Z)
• Grounded Video Situation Recognition [37.279915290069326]
  We present a novel three stage Transformer model, VideoWhisperer, that is empowered to make joint predictions. Our model operates on a group of events (clips) simultaneously and predicts verbs, verb-role pairs, their nouns, and their grounding on-the-fly.
  arXiv  Detail & Related papers  (2022-10-19T18:38:10Z)
• Learning Visual Affordance Grounding from Demonstration Videos [76.46484684007706]
  Affordance grounding aims to segment all possible interaction regions between people and objects from an image/video. We propose a Hand-aided Affordance Grounding Network (HAGNet) that leverages the aided clues provided by the position and action of the hand in demonstration videos.
  arXiv  Detail & Related papers  (2021-08-12T11:45:38Z)
• 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)
• Identity-Aware Multi-Sentence Video Description [105.13845996039277]
  We introduce an auxiliary task of Fill-in the Identity, that aims to predict persons' IDs consistently within a set of clips. One of the key components is a gender-aware textual representation as well an additional gender prediction objective in the main model. Experiments show that our proposed Fill-in the Identity model is superior to several baselines and recent works.
  arXiv  Detail & Related papers  (2020-08-22T09:50:43Z)

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.