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Found 6 papers, 4 papers with code
GroundNLQ @ Ego4D Natural Language Queries Challenge 2023
Motivated by this, we leverage a two-stage pre-training strategy to train egocentric feature extractors and the grounding model on video narrations, and further fine-tune the model on annotated data.
An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022
This technical report describes the CONE approach for Ego4D Natural Language Queries (NLQ) Challenge in ECCV 2022.
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding
This paper tackles an emerging and challenging problem of long video temporal grounding~(VTG) that localizes video moments related to a natural language (NL) query.
(Un)likelihood Training for Interpretable Embedding
Cross-modal representation learning has become a new normal for bridging the semantic gap between text and visual data.
CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval
This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus.
Ranked #1 on
Video Corpus Moment Retrieval
on TVR
vireoJD-MM at Activity Detection in Extended Videos
This notebook paper presents an overview and comparative analysis of our system designed for activity detection in extended videos (ActEV-PC) in ActivityNet Challenge 2019.
