# Distilled Siamese Networks for Visual Tracking

arXiv: https://arxiv.org/abs/1907.10586 · HTML: https://mlsanity.com/paper/1907.10586/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 30 of 34)

- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)
- [CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion](https://mlsanity.com/paper/2209.02540/) (2022)
- [ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking](https://mlsanity.com/paper/2310.10071/) (2023)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark](https://mlsanity.com/paper/2405.19818/) (2024)
- [High-Performance Transformer Tracking](https://mlsanity.com/paper/2203.13533/) (2022)
- [Reading Relevant Feature from Global Representation Memory for Visual Object Tracking](https://mlsanity.com/paper/2402.14392/) (2024)
- [Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation](https://mlsanity.com/paper/2410.11586/) (2024)
- [Correlation-Embedded Transformer Tracking: A Single-Branch Framework](https://mlsanity.com/paper/2401.12743/) (2024)
- [Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search](https://mlsanity.com/paper/2205.09676/) (2022)
- [Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering](https://mlsanity.com/paper/2306.12048/) (2023)
- [Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark](https://mlsanity.com/paper/2308.16486/) (2023)
- [Towards Class-agnostic Tracking Using Feature Decorrelation in Point Clouds](https://mlsanity.com/paper/2202.13524/) (2022)
- [Adaptive Siamese Tracking with a Compact Latent Network](https://mlsanity.com/paper/2302.00930/) (2023)
- [Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention](https://mlsanity.com/paper/2205.04326/) (2022)
- [Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning](https://mlsanity.com/paper/2209.13635/) (2022)
- [Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS](https://mlsanity.com/paper/2310.13951/) (2023)
- [LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking](https://mlsanity.com/paper/2405.17660/) (2024)
- [Hierarchical Graph Pattern Understanding for Zero-Shot VOS](https://mlsanity.com/paper/2312.09525/) (2023)
- [FARTrack: Fast Autoregressive Visual Tracking with High Performance](https://mlsanity.com/paper/2602.03214/) (2026)
- [Human Pose Transfer with Augmented Disentangled Feature Consistency](https://mlsanity.com/paper/2107.10984/) (2021)
- [An Efficient Person Clustering Algorithm for Open Checkout-free Groceries](https://mlsanity.com/paper/2208.02973/) (2022)
- [Event Stream-based Visual Object Tracking: HDETrack V2 and A High-Definition Benchmark](https://mlsanity.com/paper/2502.05574/) (2025)
- [MVTD: A Benchmark Dataset for Maritime Visual Object Tracking](https://mlsanity.com/paper/2506.02866/) (2025)
- [Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions](https://mlsanity.com/paper/2508.08352/) (2025)
- [Leveraging Text-to-Image Diffusion Models for Unsupervised Visual Object Tracking](https://mlsanity.com/paper/2605.26933/) (2026)
- [Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection](https://mlsanity.com/paper/2204.09804/) (2022)
- [Leveraging Multi-stream Information Fusion for Trajectory Prediction in Low-illumination Scenarios: A Multi-channel Graph Convolutional Approach](https://mlsanity.com/paper/2211.10226/) (2022)
- [The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised Framework](https://mlsanity.com/paper/2307.05201/) (2023)

## Abstract (arXiv)

In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to learn small, fast and accurate trackers (students), which capture critical knowledge from large Siamese trackers (teachers) by a teacher-students knowledge distillation model. This model is intuitively inspired by the one teacher vs. multiple students learning method typically employed in schools. In particular, our model contains a single teacher-student distillation module and a student-student knowledge sharing mechanism. The former is designed using a tracking-specific distillation strategy to transfer knowledge from a teacher to students. The latter is utilized for mutual learning between students to enable in-depth knowledge understanding. Extensive empirical evaluations on several popular Siamese trackers demonstrate the generality and effectiveness of our framework. Moreover, the results on five tracking benchmarks show that the proposed distilled trackers achieve compression rates of up to 18× and frame-rates of 265 FPS, while obtaining comparable tracking accuracy compared to base models.