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InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges

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arxiv 2211.09529 v1 pith:YR3YA4UK submitted 2022-11-17 cs.CV

Guo Chen , Sen Xing , Zhe Chen , Yi Wang , Kunchang Li , Yizhuo Li , Yi Liu , Jiahao Wang
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Yin-Dong Zheng Bingkun Huang Zhiyu Zhao Junting Pan Yifei Huang Zun Wang Jiashuo Yu Yinan He Hongjie Zhang Tong Lu Yali Wang Limin Wang Yu Qiao
This is my paper
classification cs.CV
keywords ego4dfivefoundationinternvideo-ego4dmodeltasksvideochampion
verification ladder T0 review T1 audit T2 compute T3 formal
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In this report, we present our champion solutions to five tracks at Ego4D challenge. We leverage our developed InternVideo, a video foundation model, for five Ego4D tasks, including Moment Queries, Natural Language Queries, Future Hand Prediction, State Change Object Detection, and Short-term Object Interaction Anticipation. InternVideo-Ego4D is an effective paradigm to adapt the strong foundation model to the downstream ego-centric video understanding tasks with simple head designs. In these five tasks, the performance of InternVideo-Ego4D comprehensively surpasses the baseline methods and the champions of CVPR2022, demonstrating the powerful representation ability of InternVideo as a video foundation model. Our code will be released at https://github.com/OpenGVLab/ego4d-eccv2022-solutions

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GazeNLQ @ Ego4D Natural Language Queries Challenge 2025

    cs.CV 2025-06 conditional novelty 5.0 of 10

    GazeNLQ adds contrastively pretrained gaze embeddings to a GroundNLQ-style grounding model, reporting 27.82 R1@0.3 on the Ego4D NLQ test split only when ensembled with GroundVQA.

  2. OSGNet @ Ego4D Episodic Memory Challenge 2025

    cs.CV 2025-06 conditional novelty 4.0 of 10

    OSGNet, an early-fusion grounding model, wins all three Ego4D Episodic Memory Challenge tracks by converting localization tasks into retrieval problems.

  3. Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision

    cs.CV 2025-06 accept novelty 3.0 of 10

    A comprehensive review of cross-view video understanding that uses both first-person and third-person cameras, organized into a three-direction taxonomy with a dataset catalog and future research gaps.

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