# Deformable Object Tracking with Gated Fusion

arXiv: https://arxiv.org/abs/1809.10417 · HTML: https://mlsanity.com/paper/1809.10417/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Tracking by Joint Local and Global Search: A Target-aware Attention based Approach](https://mlsanity.com/paper/2106.04840/) (2021)
- [VrdONE: One-stage Video Visual Relation Detection](https://mlsanity.com/paper/2408.09408/) (2024)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [DFAM-DETR: Deformable feature based attention mechanism DETR on slender object detection](https://mlsanity.com/paper/2204.10667/) (2022)
- [GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2602.14771/) (2026)

## Abstract (arXiv)

The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods.