AVHash: Joint Audio-Visual Hashing for Video Retrieval

Video hashing is a technique of encoding videos into binary vectors, facilitating efficient video storage and high-speed computation. Current approaches to video hashing predominantly utilize sequential frame images to produce semantic binary codes. However, videos encompass not only visual but also audio signals. Therefore, we propose a tri-level Transformer-based audio-visual hashing technique for video retrieval, named AVHash. It first processes audio and visual signals separately using pre-trained AST and ViT large models, and then projects temporal audio and keyframes into a shared latent semantic space using a Transformer encoder. Subsequently, a gated attention mechanism is designed to fuse the paired audio-visual signals in the video, followed by another Transformer encoder leading to the final video representation. The training of this AVHash model is directed by a video-based contrastive loss as well as a semantic alignment regularization term for audio-visual signals. Experimental results show that AVHash significantly outperforms existing video hashing methods in video retrieval tasks. Furthermore, ablation studies reveal that while video hashing based solely on visual signals achieves commendable mAP scores, the incorporation of audio signals can further boost its performance for video retrieval.