Event-Oriented State Alignment Network for Weakly Supervised Temporal Language Grounding.

Area of Science:

• Computer Vision
• Natural Language Processing
• Artificial Intelligence

Background:

• Weakly supervised temporal language grounding (TLG) aims to identify video events using text queries without explicit temporal labels.
• Current TLG methods often rely on superficial correlations, leading to inaccurate event localization due to a lack of semantic coherence.
• The need for robust methods that capture event-oriented semantic consistency across video and text modalities is critical.

Purpose of the Study:

• To develop a novel network, the Event-oriented State Alignment Network (ESAN), for improved weakly supervised temporal language grounding.
• To enhance semantic coherence within modalities and consistency across modalities for more accurate event localization.
• To address the limitations of existing methods that suffer from partial frame correlations and misleading results.

Main Methods:

• Constructed 'start-event-end' semantic state sets for both textual queries and video data.
• Employed relative entropy for cross-modal alignment via knowledge distillation from pre-trained large models.
• Leveraged vision-language models for static frame semantics and large language models for dynamic semantic changes.

Main Results:

• ESAN significantly outperformed existing methods on two benchmark datasets for temporal language grounding.
• The proposed method demonstrated a reduction in false high correlations, leading to improved overall performance.
• Achieved enhanced precision and reliability in locating events within untrimmed videos based on natural language queries.

Conclusions:

• The Event-oriented State Alignment Network (ESAN) effectively addresses the limitations of previous TLG approaches.
• ESAN's ability to capture event-oriented semantic coherence and cross-modal consistency offers a significant advancement in the field.
• The findings underscore the potential of ESAN for more accurate and reliable temporal language grounding.