Cross Modal Video Retrieval

Cross-modal video retrieval aims to find videos relevant to a given text query, bridging the gap between visual and textual information. Recent research emphasizes improving retrieval accuracy by incorporating multiple modalities (e.g., text, video, motion) and developing sophisticated models that leverage attention mechanisms and contrastive learning to better align these modalities. This focus includes optimizing feature extraction and fusion techniques, particularly within transformer-based architectures, and addressing issues like modality imbalance and partial relevance. Advances in this field have significant implications for applications such as video search engines, content recommendation systems, and assistive technologies for visually impaired individuals.