# Efficient Logic Gate Networks for Video Copy Detection

arXiv: https://arxiv.org/abs/2604.21694 · HTML: https://mlsanity.com/paper/2604.21694/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions](https://mlsanity.com/paper/2003.03488/) (2020)
- [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016)
- [Deep Differentiable Logic Gate Networks](https://mlsanity.com/paper/2210.08277/) (2022)
- [ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning](https://mlsanity.com/paper/1908.07410/) (2019)
- [Convolutional Differentiable Logic Gate Networks](https://mlsanity.com/paper/2411.04732/) (2024)
- [FIVR: Fine-grained Incident Video Retrieval](https://mlsanity.com/paper/1809.04094/) (2018)
- [DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval](https://mlsanity.com/paper/2106.13266/) (2021)
- [Self-Supervised Video Similarity Learning](https://mlsanity.com/paper/2304.03378/) (2023)
- [A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection](https://mlsanity.com/paper/2203.02654/) (2022)
- [The 2023 Video Similarity Dataset and Challenge](https://mlsanity.com/paper/2306.09489/) (2023)
- [Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval](https://mlsanity.com/paper/2312.09716/) (2023)
- [VADER: Video Alignment Differencing and Retrieval](https://mlsanity.com/paper/2303.13193/) (2023)
- [LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deployment](https://mlsanity.com/paper/2511.12340/) (2025)
- [3D-CSL: self-supervised 3D context similarity learning for Near-Duplicate Video Retrieval](https://mlsanity.com/paper/2211.05352/) (2022)
- [Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval](https://mlsanity.com/paper/2309.08167/) (2023)
- [Counteracting temporal attacks in Video Copy Detection](https://mlsanity.com/paper/2501.11171/) (2025)

## Abstract (arXiv)

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.