Fine-grained Spatiotemporal Grounding on Egocentric Videos

• URL: http://arxiv.org/abs/2508.00518v1
• Date: Fri, 01 Aug 2025 10:53:27 GMT
• Title: Fine-grained Spatiotemporal Grounding on Egocentric Videos
• Authors: Shuo Liang, Yiwu Zhong, Zi-Yuan Hu, Yeyao Tao, Liwei Wang,
• Abstract summary: We introduce EgoMask, the first pixel-level benchmark for fine-temporal grounding in egocentric videos.<n>EgoMask is constructed by our proposed automatic annotation pipeline, which annotates referring expressions and object masks.<n>We also create EgoMask-Train, a large-scale training dataset to facilitate model development.
• Score: 13.319346673043286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatiotemporal video grounding aims to localize target entities in videos based on textual queries. While existing research has made significant progress in exocentric videos, the egocentric setting remains relatively underexplored, despite its growing importance in applications such as augmented reality and robotics. In this work, we conduct a systematic analysis of the discrepancies between egocentric and exocentric videos, revealing key challenges such as shorter object durations, sparser trajectories, smaller object sizes, and larger positional shifts. To address these challenges, we introduce EgoMask, the first pixel-level benchmark for fine-grained spatiotemporal grounding in egocentric videos. It is constructed by our proposed automatic annotation pipeline, which annotates referring expressions and object masks across short-, medium-, and long-term videos. Additionally, we create EgoMask-Train, a large-scale training dataset to facilitate model development. Experiments demonstrate that the state-of-the-art spatiotemporal grounding models perform poorly on our benchmark EgoMask, but fine-tuning on EgoMask-Train yields significant improvements, while preserving performance on exocentric datasets. Our work thus provides essential resources and insights for advancing egocentric video understanding. Our code is available at https://github.com/LaVi-Lab/EgoMask .

Related papers

• PlayerOne: Egocentric World Simulator [73.88786358213694]
  PlayerOne is the first egocentric realistic world simulator.<n>It generates egocentric videos that are strictly aligned with the real scene human motion of the user captured by an exocentric camera.
  arXiv  Detail & Related papers  (2025-06-11T17:59:53Z)
• EgoM2P: Egocentric Multimodal Multitask Pretraining [55.259234688003545]
  Building large-scale egocentric multimodal and multitask models presents unique challenges.<n> EgoM2P is a masked modeling framework that learns from temporally-aware multimodal tokens to train a large, general-purpose model for egocentric 4D understanding.<n>We will fully open-source EgoM2P to support the community and advance egocentric vision research.
  arXiv  Detail & Related papers  (2025-06-09T15:59:25Z)
• Object-Shot Enhanced Grounding Network for Egocentric Video [60.97916755629796]
  We propose OSGNet, an Object-Shot enhanced Grounding Network for egocentric video.<n>Specifically, we extract object information from videos to enrich video representation.<n>We analyze the frequent shot movements inherent to egocentric videos, leveraging these features to extract the wearer's attention information.
  arXiv  Detail & Related papers  (2025-05-07T09:20:12Z)
• EgoExo-Gen: Ego-centric Video Prediction by Watching Exo-centric Videos [49.24266108952835]
  Given an exo-centric video, the first frame of the corresponding ego-centric video, and textual instructions, the goal is to generate futur frames of the ego-centric video.<n>EgoExo-Gen explicitly models the hand-object dynamics for cross-view video prediction.
  arXiv  Detail & Related papers  (2025-04-16T03:12:39Z)
• Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos [51.8995932557911]
  EgoTempo is a dataset designed to evaluate temporal understanding in the egocentric domain.<n>We show that state-of-the-art Multi-Modal Large Language Models (MLLMs) on benchmarks achieve remarkably high performance using just text or a single frame as input.<n>We hope EgoTempo will catalyze new research in the field and inspire models that better capture the complexity of temporal dynamics.
  arXiv  Detail & Related papers  (2025-03-17T18:50:36Z)
• EgoVid-5M: A Large-Scale Video-Action Dataset for Egocentric Video Generation [30.350824860817536]
  EgoVid-5M is the first high-quality dataset curated for egocentric video generation. We introduce EgoDreamer, which is capable of generating egocentric videos driven simultaneously by action descriptions and kinematic control signals.
  arXiv  Detail & Related papers  (2024-11-13T07:05:40Z)
• Object Aware Egocentric Online Action Detection [23.504280692701272]
  We introduce an Object-Aware Module that integrates egocentric-specific priors into existing Online Action Detection frameworks. Our work can be seamlessly integrated into existing models with minimal overhead and bring consistent performance enhancements.
  arXiv  Detail & Related papers  (2024-06-03T07:58:40Z)
• Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions? [48.702973928321946]
  Egocentric video-language pretraining is a crucial step in advancing the understanding of hand-object interactions in first-person scenarios.<n>Despite successes on existing testbeds, we find that current EgoVLMs can be easily misled by simple modifications.<n>This raises the question: Do EgoVLMs truly understand hand-object interactions?
  arXiv  Detail & Related papers  (2024-05-28T00:27:29Z)
• Retrieval-Augmented Egocentric Video Captioning [53.2951243928289]
  EgoInstructor is a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos. We train the cross-view retrieval module with a novel EgoExoNCE loss that pulls egocentric and exocentric video features closer by aligning them to shared text features that describe similar actions.
  arXiv  Detail & Related papers  (2024-01-01T15:31:06Z)
• EgoTracks: A Long-term Egocentric Visual Object Tracking Dataset [19.496721051685135]
  Embodied tracking is a key component to many egocentric vision problems. EgoTracks is a new dataset for long-term egocentric visual object tracking. We show improvements that can be made to a STARK tracker to significantly increase its performance on egocentric data.
  arXiv  Detail & Related papers  (2023-01-09T09:10:35Z)

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.