# GradNet: Gradient-Guided Network for Visual Object Tracking

arXiv: https://arxiv.org/abs/1909.06800 · HTML: https://mlsanity.com/paper/1909.06800/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2019.

## Built on by (most cited first, 30 of 41)

- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark](https://mlsanity.com/paper/2103.16746/) (2021)
- [Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking](https://mlsanity.com/paper/2203.05328/) (2022)
- [STMTrack: Template-free Visual Tracking with Space-time Memory Networks](https://mlsanity.com/paper/2104.00324/) (2021)
- [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021)
- [Learn to Match: Automatic Matching Network Design for Visual Tracking](https://mlsanity.com/paper/2108.00803/) (2021)
- [GLiT: Neural Architecture Search for Global and Local Image Transformer](https://mlsanity.com/paper/2107.02960/) (2021)
- [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://mlsanity.com/paper/2112.02838/) (2021)
- [Unified Transformer Tracker for Object Tracking](https://mlsanity.com/paper/2203.15175/) (2022)
- [Saliency-Associated Object Tracking](https://mlsanity.com/paper/2108.03637/) (2021)
- [Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization](https://mlsanity.com/paper/2211.10528/) (2022)
- [Active Learning for Deep Visual Tracking](https://mlsanity.com/paper/2110.13259/) (2021)
- [Reading Relevant Feature from Global Representation Memory for Visual Object Tracking](https://mlsanity.com/paper/2402.14392/) (2024)
- [Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking](https://mlsanity.com/paper/2104.03114/) (2021)
- [SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking](https://mlsanity.com/paper/2105.11237/) (2021)
- [Distractor-aware Event-based Tracking](https://mlsanity.com/paper/2310.14194/) (2023)
- [Cross-Modal Object Tracking via Modality-Aware Fusion Network and A Large-Scale Dataset](https://mlsanity.com/paper/2312.14446/) (2023)
- [Cross-Modal Object Tracking: Modality-Aware Representations and A Unified Benchmark](https://mlsanity.com/paper/2111.04264/) (2021)
- [Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking](https://mlsanity.com/paper/2108.04521/) (2021)
- [Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search](https://mlsanity.com/paper/2205.09676/) (2022)
- [Adaptive Siamese Tracking with a Compact Latent Network](https://mlsanity.com/paper/2302.00930/) (2023)
- [A Discriminative Single-Shot Segmentation Network for Visual Object Tracking](https://mlsanity.com/paper/2112.11846/) (2021)
- [BACTrack: Building Appearance Collection for Aerial Tracking](https://mlsanity.com/paper/2312.06136/) (2023)
- [Equivalence of Correlation Filter and Convolution Filter in Visual Tracking](https://mlsanity.com/paper/2105.00158/) (2021)
- [SiamTHN: Siamese Target Highlight Network for Visual Tracking](https://mlsanity.com/paper/2303.12304/) (2023)
- [DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking](https://mlsanity.com/paper/2104.11585/) (2021)
- [Updatable Siamese Tracker with Two-stage One-shot Learning](https://mlsanity.com/paper/2104.15049/) (2021)
- [Leveraging the Power of Data Augmentation for Transformer-based Tracking](https://mlsanity.com/paper/2309.08264/) (2023)
- [Real-Time Visual Object Tracking via Few-Shot Learning](https://mlsanity.com/paper/2103.10130/) (2021)

## Abstract (arXiv)

The fully-convolutional siamese network based on template matching has shown great potentials in visual tracking. During testing, the template is fixed with the initial target feature and the performance totally relies on the general matching ability of the siamese network. However, this manner cannot capture the temporal variations of targets or background clutter. In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations. Our algorithm performs feed-forward and backward operations to exploit the discriminative informaiton in gradients and capture the core attention of the target. To be specific, the algorithm can utilize the information from the gradient to update the template in the current frame. In addition, a template generalization training method is proposed to better use gradient information and avoid overfitting. To our knowledge, this work is the first attempt to exploit the information in the gradient for template update in siamese-based trackers. Extensive experiments on recent benchmarks demonstrate that our method achieves better performance than other state-of-the-art trackers.