Papers with bottom-up models

1 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.

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