Exploring Language Hierarchy for Video Grounding.

Area of Science:

• Computer Science
• Artificial Intelligence
• Natural Language Processing

Background:

• Video grounding requires precise moment localization based on text queries.
• Language understanding is crucial, with natural hierarchies in clauses and modifiers.
• Current models often neglect main clauses, focusing on discriminative modifiers, leading to localization errors.

Purpose of the Study:

• To address the negligence of main clauses in video grounding.
• To propose a novel Hierarchical Language Network (HLN) and Multi-Instance Positive-Unlabelled Learning (MI-PUL).
• To improve the accurate localization of target moments in videos.

Main Methods:

• Developed a Hierarchical Language Network (HLN) to exploit language hierarchy across different semantic layers.
• Introduced Multi-Instance Positive-Unlabelled Learning (MI-PUL) to learn from background clips without explicit labels.
• Integrated HLN and MI-PUL to enhance the learning of main clauses for video grounding.

Main Results:

• Empirically demonstrated the phenomenon of main clause negligence in existing video grounding models.
• HLN allows attention to be distributed across various language hierarchy layers.
• MI-PUL enables learning from potentially relevant background clips, improving main clause understanding.
• Achieved a 6.15% gain in [Formula: see text] on the TACoS dataset, significantly improving state-of-the-art methods.

Conclusions:

• The proposed HLN and MI-PUL effectively address the challenge of main clause negligence in video grounding.
• The combined approach enhances the learning of crucial semantic information for accurate moment localization.
• HLN can be integrated into existing methods, providing significant performance improvements on challenging datasets.