The most important effect of the video hashing technique is to support fast retrieval, which is benefiting from the high efficiency of binary calculation. Current video hash approaches are thus mainly targeted at learning compact binary codes to represent video content accurately. However, they may overlook the generation efficiency for hash codes, i.e., designing lightweight neural networks. This paper proposes anEfficientUnsupervisedVideoHashing (EUVH)method, which is not only for computing compact hash codes but also for designing a lightweight deep model. Specifically, we present an MLP-based model, where the video tensor is split into several groups and multiple axial contexts are explored to separately refine them in parallel. The axial contexts are referred to as the dynamics aggregated from different axial scales, including long/middle/short-range dependencies. The group operation significantly reduces the computational cost of the MLP backbone. Moreover, to achieve compact video hash codes, three structural losses are utilized. As demonstrated by the experiment, the three structures are highly complementary for approximating the real data structure. We conduct extensive experiments on three benchmark datasets for the unsupervised video hashing task and show the superior trade-off between performance and computational cost of our EUVH to the state of the arts.

Unsupervised video hashing typically aims to learn a compact binary vector to represent complex video content without using manual annotations. Existing unsupervised hashing methods generally suffer from incomplete exploration of various perspective dependencies (e.g., long-range and short-range) and data structures that exist in visual contents, resulting in less discriminative hash codes. In this paper, we propose aMulti-granularity Contextualized and Multi-Structure preserved Hashing (MCMSH) method, exploring multiple axial contexts for discriminative video representation generation and various structural information for unsupervised learning simultaneously. Specifically, we delicately design three self-gating modules to separately model three granularities of dependencies (i.e., long/middle/short-range dependencies) and densely integrate them into MLP-Mixer for feature contextualization, leading to a novel model MC-MLP. To facilitate unsupervised learning, we investigate three kinds of data structures, including clusters, local neighborhood similarity structure, and inter/intra-class variations, and design a multi-objective task to train MC-MLP. These data structures show high complementarities in hash code learning. We conduct extensive experiments using three video retrieval benchmark datasets, demonstrating that our MCMSH not only boosts the performance of the backbone MLP-Mixer significantly but also outperforms the competing methods notably. Code is available at: https://github.com/haoyanbin918/MCMSH.