# Multi-Task Driven Feature Models for Thermal Infrared Tracking

arXiv: https://arxiv.org/abs/1911.11384 · HTML: https://mlsanity.com/paper/1911.11384/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 10 of 10)

- [Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking](https://mlsanity.com/paper/2201.08673/) (2022)
- [Position-Aware Relation Learning for RGB-Thermal Salient Object Detection](https://mlsanity.com/paper/2209.10158/) (2022)
- [Self-Supervised Tracking via Target-Aware Data Synthesis](https://mlsanity.com/paper/2106.10900/) (2021)
- [SiamCorners: Siamese Corner Networks for Visual Tracking](https://mlsanity.com/paper/2104.07303/) (2021)
- [Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking](https://mlsanity.com/paper/2108.00187/) (2021)
- [Learning Twofold Heterogeneous Multi-Task by Sharing Similar Convolution Kernel Pairs](https://mlsanity.com/paper/2101.12431/) (2021)
- [Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task](https://mlsanity.com/paper/2201.02305/) (2022)
- [Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network](https://mlsanity.com/paper/2104.01032/) (2021)
- [PixelGame: Infrared small target segmentation as a Nash equilibrium](https://mlsanity.com/paper/2205.13124/) (2022)
- [DCFG: Diverse Cross-Channel Fine-Grained Feature Learning and Progressive Fusion Siamese Tracker for Thermal Infrared Target Tracking](https://mlsanity.com/paper/2504.14311/) (2025)

## Abstract (arXiv)

Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framework to learn the TIR-specific discriminative features and fine-grained correlation features for TIR tracking. Specifically, we first use an auxiliary classification network to guide the generation of TIR-specific discriminative features for distinguishing the TIR objects belonging to different classes. Second, we design a fine-grained aware module to capture more subtle information for distinguishing the TIR objects belonging to the same class. These two kinds of features complement each other and recognize TIR objects in the levels of inter-class and intra-class respectively. These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task. In addition, we develop a large-scale TIR training dataset to train the network for adapting the model to the TIR domain. Extensive experimental results on three benchmarks show that the proposed algorithm achieves a relative gain of 10% over the baseline and performs favorably against the state-of-the-art methods. Codes and the proposed TIR dataset are available at {https://github.com/QiaoLiuHit/MMNet}.