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ObjectNLQ @ Ego4D Episodic Memory Challenge 2024

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arxiv 2406.15778 v2 pith:F7UASITF submitted 2024-06-22 cs.CV cs.AI

Yisen Feng , Haoyu Zhang , Yuquan Xie , Zaijing Li , Meng Liu , Liqiang Nie This is my paper · ORCID
classification cs.CVcs.AI
keywords objectnlqchallengeapproachego4depisodicgoalinformationlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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In this report, we present our approach for the Natural Language Query track and Goal Step track of the Ego4D Episodic Memory Benchmark at CVPR 2024. Both challenges require the localization of actions within long video sequences using textual queries. To enhance localization accuracy, our method not only processes the temporal information of videos but also identifies fine-grained objects spatially within the frames. To this end, we introduce a novel approach, termed ObjectNLQ, which incorporates an object branch to augment the video representation with detailed object information, thereby improving grounding efficiency. ObjectNLQ achieves a mean R@1 of 23.15, ranking 2nd in the Natural Language Queries Challenge, and gains 33.00 in terms of the metric R@1, IoU=0.3, ranking 3rd in the Goal Step Challenge. Our code will be released at https://github.com/Yisen-Feng/ObjectNLQ.

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Cited by 4 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. Technical Report for Ego4D Long-Term Action Anticipation Challenge 2025

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A three-stage pipeline using the EgoVideo-V encoder, a verb-noun co-occurrence reranker, SAM2 hand-object features, and a fine-tuned Llama 2 model took first place in the Ego4D 2025 long-term action anticipation challenge.

  4. HCQA-1.5 @ Ego4D EgoSchema Challenge 2025

    cs.CV 2025-05 conditional novelty 4.0 of 10

    An ensemble of LLMs with confidence filtering and low-confidence re-reasoning reaches 77% accuracy on the EgoSchema benchmark, up from 75% for the prior HCQA system.

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