Papers with natural language video localization

2 papers
DEBUG: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization (D19-1)

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Challenge: Existing models for natural language video localization are top-down and bottom-up . however, both approaches suffer several limitations, leading to performance degradation .
Approach: They propose a top-down approach for localizing a natural language description in a video sequence . they propose 'DEnse Bottom-Up Grounding' which uses the temporal boundaries of each video frame .
Outcome: The proposed framework matches the speed of top-down models while surpassing the state-of-the-art models.
Span-based Localizing Network for Natural Language Video Localization (2020.acl-main)

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Challenge: Existing approaches to NLVL are either ranking tasks or regressing the target video span.
Approach: They propose a video span localizing network to solve a natural language video localization task using a span-based QA approach.
Outcome: The proposed network outperforms the state-of-the-art methods on three benchmark datasets.

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