Progressive Scaling Visual Object Tracking

• URL: http://arxiv.org/abs/2505.19990v2
• Date: Wed, 28 May 2025 11:35:26 GMT
• Title: Progressive Scaling Visual Object Tracking
• Authors: Jack Hong, Shilin Yan, Zehao Xiao, Jiayin Cai, Xiaolong Jiang, Yao Hu, Henghui Ding,
• Abstract summary: We propose a progressive scaling training strategy for visual object tracking, systematically analyzing the influence of training data volume, model size, and input resolution on tracking performance.<n>Our empirical study reveals that while scaling each factor leads to significant improvements in tracking accuracy, naive training suffers from suboptimal optimization and limited iterative refinement.<n>We introduce DT-Training, a progressive scaling framework that integrates small teacher transfer and dual-branch alignment to maximize model potential.
• Score: 38.28834233600855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a progressive scaling training strategy for visual object tracking, systematically analyzing the influence of training data volume, model size, and input resolution on tracking performance. Our empirical study reveals that while scaling each factor leads to significant improvements in tracking accuracy, naive training suffers from suboptimal optimization and limited iterative refinement. To address this issue, we introduce DT-Training, a progressive scaling framework that integrates small teacher transfer and dual-branch alignment to maximize model potential. The resulting scaled tracker consistently outperforms state-of-the-art methods across multiple benchmarks, demonstrating strong generalization and transferability of the proposed method. Furthermore, we validate the broader applicability of our approach to additional tasks, underscoring its versatility beyond tracking.

Related papers

• From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimization [7.531052649961168]
  Reinforcement learning with verifiable rewards (RLVR) has recently advanced the reasoning capabilities of large language models (LLMs)<n>We investigate RLVR from a sample-centric perspective and introduce LPPO, a framework of progressive optimization techniques.<n>Our work addresses a critical question: how to best leverage a small set of trusted, high-quality demonstrations, rather than simply scaling up data volume.
  arXiv  Detail & Related papers  (2025-07-09T06:05:28Z)
• DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning [19.84386060857712]
  This paper introduces DiffTORI, which utilizes Differentiable Trajectory optimization as the policy representation to generate actions for deep Reinforcement and Imitation learning.<n>Across 15 model-based RL tasks and 35 imitation learning tasks with high-dimensional image and point cloud inputs, DiffTORI outperforms prior state-of-the-art methods in both domains.
  arXiv  Detail & Related papers  (2024-02-08T05:26:40Z)
• Leveraging the Power of Data Augmentation for Transformer-based Tracking [64.46371987827312]
  We propose two data augmentation methods customized for tracking. First, we optimize existing random cropping via a dynamic search radius mechanism and simulation for boundary samples. Second, we propose a token-level feature mixing augmentation strategy, which enables the model against challenges like background interference.
  arXiv  Detail & Related papers  (2023-09-15T09:18:54Z)
• Towards Sequence-Level Training for Visual Tracking [60.95799261482857]
  This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning. Four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training.
  arXiv  Detail & Related papers  (2022-08-11T13:15:36Z)
• Crop-Transform-Paste: Self-Supervised Learning for Visual Tracking [137.26381337333552]
  In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data. Since the object state is known in all synthesized data, existing deep trackers can be trained in routine ways without human annotation.
  arXiv  Detail & Related papers  (2021-06-21T07:40:34Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)
• Dynamic Scale Training for Object Detection [111.33112051962514]
  We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection. Experimental results demonstrate the efficacy of our proposed DST towards scale variation handling. It does not introduce inference overhead and could serve as a free lunch for general detection configurations.
  arXiv  Detail & Related papers  (2020-04-26T16:48:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.