Encode The Unseen: Predictive Video Hashing For Scalable Mid-stream Retrieval

Abstract

This paper tackles a new problem in computer vision: mid-stream video-to-video retrieval. This task, which consists in searching a database for content similar to a video right as it is playing, e.g. from a live stream, exhibits challenging characteristics. Only the beginning part of the video is available as query and new frames are constantly added as the video plays out. To perform retrieval in this demanding situation, we propose an approach based on a binary encoder that is both predictive and incremental in order to (1) account for the missing video content at query time and (2) keep up with repeated, continuously evolving queries throughout the streaming. In particular, we present the first hashing framework that infers the unseen future content of a currently playing video. Experiments on FCVID and ActivityNet demonstrate the feasibility of this task. Our approach also yields a significant mAP@20 performance increase compared to a baseline adapted from the literature for this t

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keyyu2020encode

Related papers

• Self-supervised Video Hashing With Hierarchical Binary Auto-encoder (2018)17.81
• Dual-stream Knowledge-preserving Hashing For Unsupervised Video Retrieval (2023)9.23
• CHAIN: Exploring Global-local Spatio-temporal Information For Improved Self-supervised Video Hashing (2023)8.60
• Video Retrieval Based On Deep Convolutional Neural Network (2017)9.03
• Deep Heterogeneous Hashing For Face Video Retrieval (2019)9.92
• Multi-focused Video Group Activities Hashing (2025)0.00
• Exploiting Local Indexing And Deep Feature Confidence Scores For Fast Image-to-video Search (2018)2.26
• Autossvh: Exploring Automated Frame Sampling For Efficient Self-supervised Video Hashing (2025)7.92