3D Self-Attention for Unsupervised Video Quantization

Unsupervised video quantization is to compress the original videos to compact binary codes so that video retrieval can be conducted in an efficient way. In this paper, we make a first attempt to combine quantization method with video retrieval called 3D-UVQ, which obtains high retrieval accuracy with low storage cost. In the proposed framework, we address two main problems: 1) how to design an effective pipeline to perceive video contextual information for video features extraction; and 2) how to quantize these features for efficient retrieval. To tackle these problems, we propose a 3D self-attention module to exploit the spatial and temporal contextual information, where each pixel is influenced by its surrounding pixels. By taking a further recurrent operation, each pixel can finally capture the global context from all pixels. Then, we propose gradient-based residual quantization which consists of several quantization blocks to approximate the features gradually. Extensive experimental results on three benchmark datasets demonstrate that our method significantly outperforms the state-of-the-arts. Ablation study shows that both the 3D self-attention module and the gradient-based residual quantization can improve the performance of retrieval. Our model is publicly available at https://github.com/brownwolf/3D-UVQ.