Compressing videos into binary codes can improve retrieval speed and reduce
storage overhead. However, learning accurate hash codes for video retrieval can
be challenging due to high local redundancy and complex global dependencies
between video frames, especially in the absence of labels. Existing
self-supervised video hashing methods have been effective in designing
expressive temporal encoders, but have not fully utilized the temporal dynamics
and spatial appearance of videos due to less challenging and unreliable
learning tasks. To address these challenges, we begin by utilizing the
contrastive learning task to capture global spatio-temporal information of
videos for hashing. With the aid of our designed augmentation strategies, which
focus on spatial and temporal variations to create positive pairs, the learning
framework can generate hash codes that are invariant to motion, scale, and
viewpoint. Furthermore, we incorporate two collaborative learning tasks, i.e.,
frame order verification and scene change regularization, to capture local
spatio-temporal details within video frames, thereby enhancing the perception
of temporal structure and the modeling of spatio-temporal relationships. Our
proposed Contrastive Hashing with Global-Local Spatio-temporal Information
(CHAIN) outperforms state-of-the-art self-supervised video hashing methods on
four video benchmark datasets. Our codes will be released.

通过对视频进行二进制编码可以提高检索速度并减少存储开销，本文提出了一种新的自监督视频哈希方法，通过对全局时空信息和局部时空细节的学习，生成对于运动、尺度和视点不变的哈希码。实验证明，该方法在四个视频基准数据集上优于现有方法。

CHAIN: 探索全球 - 局部时空信息以改进自监督视频哈希

CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved  Self-Supervised Video Hashing

Self-Supervised Video Hashing (SSVH) models learn to generate short binary
representations for videos without ground-truth supervision, facilitating
large-scale video retrieval efficiency and attracting increasing research
attention. The success of SSVH lies in the understanding of video content and
the ability to capture the semantic relation among unlabeled videos. Typically,
state-of-the-art SSVH methods consider these two points in a two-stage training
pipeline, where they firstly train an auxiliary network by instance-wise
mask-and-predict tasks and secondly train a hashing model to preserve the
pseudo-neighborhood structure transferred from the auxiliary network. This
consecutive training strategy is inflexible and also unnecessary. In this
paper, we propose a simple yet effective one-stage SSVH method called ConMH,
which incorporates video semantic information and video similarity relationship
understanding in a single stage. To capture video semantic information for
better hashing learning, we adopt an encoder-decoder structure to reconstruct
the video from its temporal-masked frames. Particularly, we find that a higher
masking ratio helps video understanding. Besides, we fully exploit the
similarity relationship between videos by maximizing agreement between two
augmented views of a video, which contributes to more discriminative and robust
hash codes. Extensive experiments on three large-scale video datasets (i.e.,
FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art
results. Code is available at this https URL.

本研究提出了名为 ConMH 的一阶段自监督视频哈希方法，该方法通过采用编码器 - 解码器结构重构视频以捕获视频语义信息，并通过最大化两个视频增强视图之间的协议以充分利用视频之间 的相似性关系。实验结果表明 ConMH 方法在大规模视频数据集上表现出色，是当前最佳方法之一。