Learning Temporal Sentence Grounding From Narrated EgoVideos
Published in
British Machine Vision Conference(2023)External Links:
TL
TL;DR
This paper proposes to artificially merge clips to train for temporal grounding in a contrastive manner using text-conditioning attention and this Clip Merging (CliMer) approach is shown to be effective when compared with a high performing TSG method.
Abstract
The onset of long-form egocentric datasets such as Ego4D and EPIC-Kitchens presents a new challenge for the task of Temporal Sentence Grounding (TSG). Compared to traditional benchmarks on which this task is evaluated, these datasets offer finer-grained sentences to ground in notably longer videos. In this paper, we develop an approach for learning to ground sentences in these datasets using only narrations and their corresponding rough narration timestamps. We propose to artificially merge clips to train for temporal grounding in a contrastive manner using text-conditioning attention. This Clip Merging (CliMer) approach is shown to be effective when compared with a high performing TSG method -- e.g. mean R@1 improves from 3.9 to 5.7 on Ego4D and from 10.7 to 13.0 on EPIC-Kitchens. Code and data splits available from: https://github.com/keflanagan/CliMer
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Localizing Moments in Long Video Via Multimodal Guidance
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Explore Inter-contrast between Videos via Composition for Weakly Supervised Temporal Sentence Grounding
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Weakly Supervised Video Moment Localization with Contrastive Negative Sample Mining
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