Efficient Logic Gate Networks for Video Copy Detection

TL;DR

This paper introduces a logic gate network-based framework for video copy detection that offers high accuracy with significantly smaller descriptors and faster inference speeds, suitable for large-scale applications.

Contribution

The work presents a novel differentiable Logic Gate Network approach that replaces traditional feature extractors with compact, logic-based representations for efficient video copy detection.

Findings

LGN models achieve competitive or better accuracy than prior methods.

Descriptors are several orders of magnitude smaller than traditional models.

Inference speeds exceed 11,000 samples per second.

Abstract

Video copy detection requires robust similarity estimation under diverse visual distortions while operating at very large scale. Although deep neural networks achieve strong performance, their computational cost and descriptor size limit practical deployment in high-throughput systems. In this work, we propose a video copy detection framework based on differentiable Logic Gate Networks (LGNs), which replace conventional floating-point feature extractors with compact, logic-based representations. Our approach combines aggressive frame miniaturization, binary preprocessing, and a trainable LGN embedding model that learns both logical operations and interconnections. After training, the model can be discretized into a purely Boolean circuit, enabling extremely fast and memory-efficient inference. We systematically evaluate different similarity strategies, binarization schemes, and LGN architectures across multiple dataset folds and difficulty levels. Experimental results demonstrate that LGN-based models achieve competitive or superior accuracy and ranking performance compared to prior models, while producing descriptors several orders of magnitude smaller and delivering inference speeds exceeding 11k samples per second. These findings indicate that logic-based models offer a promising alternative for scalable and resource-efficient video copy detection.