A cross-modal conditional mechanism based on attention for text-video retrieval

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Current cross-modal retrieval often aligns only global video and sentence features.
• Videos contain richer information than text, necessitating finer-grained matching.
• Existing methods may miss critical frame-level details crucial for accurate text-video correspondence.

Purpose of the Study:

• To develop an advanced cross-modal model for text-video retrieval.
• To improve matching by focusing on frame-level semantics and detailed information.
• To enhance the understanding of video content through text-driven feature extraction.

Main Methods:

• Proposed a cross-modal conditional feature aggregation model utilizing an attention mechanism.
• Introduced a cross-modal attentional feature aggregation module for extracting relevant frame features guided by text semantics.
• Implemented a global-local similarity calculation module for dual-granularity matching (video-sentence and frame-word).

Main Results:

• The model demonstrated superior performance over state-of-the-art methods on four benchmark datasets (MSR-VTT, LSMDC, MSVD, DiDeMo).
• The cross-modal attention aggregation effectively captured primary semantic video information.
• The global-local similarity calculation accurately matched text and video based on both topic and detail features.

Conclusions:

• The proposed model significantly advances text-video retrieval capabilities.
• Attention-based feature aggregation is effective for capturing salient video semantics.
• Dual-granularity similarity calculation enhances the precision of text-video matching.