Dual Encoding for Video Retrieval by Text.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Video retrieval by text requires effective cross-modal matching between video frames and natural language queries.
• Existing methods often use single-level encoders and struggle with common space learning.

Purpose of the Study:

• To propose a novel dual deep encoding network for video retrieval by text.
• To develop a hybrid space learning approach for improved cross-modal matching.

Main Methods:

• A dual deep encoding network is proposed to represent videos and queries in dense vector spaces.
• Multi-level encoding captures coarse-to-fine details of both modalities.
• Hybrid space learning combines latent and concept spaces for enhanced performance and interpretability.

Main Results:

• The proposed dual encoding network achieves effective sequence-to-sequence cross-modal matching.
• Experiments on four challenging video datasets demonstrate the method's viability.
• The hybrid space learning approach shows practical effectiveness.

Conclusions:

• The dual deep encoding network with hybrid space learning offers a powerful solution for text-based video retrieval.
• This approach provides a conceptually simple yet practically effective end-to-end trained system.
• Further research can build upon multi-level encoding and hybrid space learning for cross-modal retrieval tasks.