Pith. sign in

REVIEW 5 cited by

OSGNet @ Ego4D Episodic Memory Challenge 2025

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2506.03710 v1 pith:K2YIASK5 submitted 2025-06-04 cs.CV cs.AI

OSGNet @ Ego4D Episodic Memory Challenge 2025

Yisen Feng , Haoyu Zhang , Qiaohui Chu , Meng Liu , Weili Guan , Yaowei Wang , Liqiang Nie This is my paper
classification cs.CV cs.AI
keywords localizationvideotrackschallengeego4degocentricepisodicmemory
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

In this report, we present our champion solutions for the three egocentric video localization tracks of the Ego4D Episodic Memory Challenge at CVPR 2025. All tracks require precise localization of the interval within an untrimmed egocentric video. Previous unified video localization approaches often rely on late fusion strategies, which tend to yield suboptimal results. To address this, we adopt an early fusion-based video localization model to tackle all three tasks, aiming to enhance localization accuracy. Ultimately, our method achieved first place in the Natural Language Queries, Goal Step, and Moment Queries tracks, demonstrating its effectiveness. Our code can be found at https://github.com/Yisen-Feng/OSGNet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

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

  1. HiERO-StepG @ Ego4D Step Grounding Challenge: hierarchical activity understanding enables zero-shot step grounding

    cs.CV 2026-05 unverdicted novelty 4.0

    HiERO-StepG augments weakly-supervised HiERO features with level agreement, temporal monotonicity, and noise reduction to reach 56.27% R@1 (IoU=0.3) and second place on the Ego4D Step Grounding challenge without proce...

  2. JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026

    cs.CV 2026-05 unverdicted novelty 3.0

    JFAA freezes a JEPA future-prediction model, adds a lightweight probe and ensemble, and wins the 2026 EK-100 action anticipation challenge.

  3. OSGNet with MLLM Reranking @ Ego4D Episodic Memory Challenge 2026

    cs.CV 2026-05 accept novelty 3.0

    A hybrid pipeline of OSGNet candidate generation followed by MLLM reranking secured first place in both the Natural Language Queries and GoalStep tracks of the Ego4D Episodic Memory Challenge.

  4. MARS: Technical Report for the CASTLE Challenge at EgoVis 2026

    cs.CV 2026-05 unverdicted novelty 3.0

    MARS converts long videos to captions and summaries, maintains modality-specific memories, and deploys an agent to select evidence or answer, placing second on the CASTLE Challenge leaderboard.

  5. VISTA: Technical Report for the Ego4D Short-Term Object Interaction Anticipation at EgoVis 2026

    cs.CV 2026-05 unverdicted novelty 2.0

    VISTA wins first place on the Ego4D Short-Term Object Interaction Anticipation challenge by combining spatial object proposals with temporal context via feature modulation and ROI fusion, followed by ensembling.