PROBE: VLM-Guided Discrete Structural Reconfiguration for Customized and Efficient Video Retrieval

Abstract

Existing self-supervised video hashing methods achieve high efficiency by encoding videos into compact binary representations, but they typically rely on a fixed global similarity geometry that enforces a single notion of similarity across all queries. In many real-world retrieval scenarios, however, the same videos may need to be compared under different semantic criteria, such as action, scene, object, emotion, or intent. This requirement gives rise to criterion-dependent video comparison, which remains largely unexplored in efficient hashing-based retrieval frameworks. To this end, we propose an efficient VLM-guided video retrieval paradigm PROBE that leverages a frozen vision–language model (VLM) to inject rich semantic structure into a reusable hash index. Each video is encoded once into a compact binary representation, while prompts act as discrete operators that select criterion-relevant semantic subspaces within the hash space, inducing discrete structural reconfiguration of similarity geometry and enabling customized video–video comparison via efficient masked distance computation. Guided by prompt-conditioned representations from the vision–language teacher, we introduce criterion-induced subspace regularization that transfers semantic geometry into criterion-conditioned hash subspaces while preserving local relational consistency. We further employ a global unconditional alignment and reconstruction objective to stabilize the full hash space across views and datasets. A single model trained jointly on heterogeneous video datasets consistently outperforms strong video hashing baselines and generalizes effectively to unseen datasets and diverse retrieval criteria, demonstrating that VLM-guided discrete structural reconfiguration is key to efficient, customized, and reusable video retrieval. The source code is available at https://github.com/liamgu06/PROBE.