# CFTrack: Enhancing Lightweight Visual Tracking through Contrastive Learning and Feature Matching

arXiv: https://arxiv.org/abs/2502.19705 · HTML: https://mlsanity.com/paper/2502.19705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 32 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Ocean: Object-aware Anchor-free Tracking](https://mlsanity.com/paper/2006.10721/) (2020)
- [LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search](https://mlsanity.com/paper/2104.14545/) (2021)
- [Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation](https://mlsanity.com/paper/2007.02024/) (2020)
- [HiFT: Hierarchical Feature Transformer for Aerial Tracking](https://mlsanity.com/paper/2108.00202/) (2021)
- [TCTrack: Temporal Contexts for Aerial Tracking](https://mlsanity.com/paper/2203.01885/) (2022)
- [Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking](https://mlsanity.com/paper/2308.06904/) (2023)
- [Efficient Visual Tracking with Exemplar Transformers](https://mlsanity.com/paper/2112.09686/) (2021)
- [FEAR: Fast, Efficient, Accurate and Robust Visual Tracker](https://mlsanity.com/paper/2112.07957/) (2021)
- [Towards Real-World Visual Tracking with Temporal Contexts](https://mlsanity.com/paper/2308.10330/) (2023)
- [Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking](https://mlsanity.com/paper/2308.11450/) (2023)
- [Fast-Tracker: A Robust Aerial System for Tracking Agile Target in Cluttered Environments](https://mlsanity.com/paper/2011.03968/) (2020)
- [Mobile Vision Transformer-based Visual Object Tracking](https://mlsanity.com/paper/2309.05829/) (2023)

## Built on by (most cited first, 1 of 1)

- [MANTA: Physics-Informed Generalized Underwater Object Tracking](https://mlsanity.com/paper/2511.23405/) (2025)

## Abstract (arXiv)

Achieving both efficiency and strong discriminative ability in lightweight visual tracking is a challenge, especially on mobile and edge devices with limited computational resources. Conventional lightweight trackers often struggle with robustness under occlusion and interference, while deep trackers, when compressed to meet resource constraints, suffer from performance degradation. To address these issues, we introduce CFTrack, a lightweight tracker that integrates contrastive learning and feature matching to enhance discriminative feature representations. CFTrack dynamically assesses target similarity during prediction through a novel contrastive feature matching module optimized with an adaptive contrastive loss, thereby improving tracking accuracy. Extensive experiments on LaSOT, OTB100, and UAV123 show that CFTrack surpasses many state-of-the-art lightweight trackers, operating at 136 frames per second on the NVIDIA Jetson NX platform. Results on the HOOT dataset further demonstrate CFTrack's strong discriminative ability under heavy occlusion.