Transformer_Tracking

Transformer_Tracking

视觉追踪中Transformer应用的全面综述和前沿动态

本项目汇总了Transformer在视觉追踪领域的应用进展,包括统一追踪、单目标追踪和3D单目标追踪等方向。内容涵盖最新研究论文、技术趋势分析、基准测试结果以及学习资源,为相关研究人员和从业者提供全面的参考信息。重点关注自回归时序建模、联合特征提取与交互等前沿技术,展现了视觉追踪的最新发展动态。

Transformer视觉跟踪目标检测计算机视觉深度学习Github开源项目

Transformer Tracking

This repository is a paper digest of Transformer-related approaches in visual tracking tasks. Currently, tasks in this repository include Unified Tracking (UT), Single Object Tracking (SOT) and 3D Single Object Tracking (3DSOT). Note that some trackers involving a Non-Local attention mechanism are also collected. Papers are listed in alphabetical order of the first character.

:link:Jump to:

[!NOTE] I find it hard to trace all tasks that are related to tracking, including Video Object Segmentation (VOS), Multiple Object Tracking (MOT), Video Instance Segmentation (VIS), Video Object Detection (VOD) and Object Re-Identification (ReID). Hence, I discard all other tracking tasks in a previous update. If you are interested, you can find plenty of collections in this archived version. Besides, the most recent trend shows that different tracking tasks are coming to the same avenue.

:star2:Recommendation

It's the End of the Game

State-of-the-Art Transformer Tracker:two_hearts::two_hearts::two_hearts:

  • GRM (Generalized Relation Modeling for Transformer Tracking) [paper] [code] [video]
  • AiATrack (AiATrack: Attention in Attention for Transformer Visual Tracking) [paper] [code] [video]

Up-to-Date Benchmark Results:rocket::rocket::rocket:

Helpful Learning Resource for Tracking:thumbsup::thumbsup::thumbsup:

  • (Survey) Transformers in Single Object Tracking: An Experimental Survey [paper], Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook [paper]
  • (Talk) Discriminative Appearance-Based Tracking and Segmentation [video], Deep Visual Reasoning with Optimization-Based Network Modules [video]
  • (Library) PyTracking: Visual Tracking Library Based on PyTorch [code]
  • (People) Martin Danelljan@ETH [web], Bin Yan@DLUT [web]

Recent Trends:fire::fire::fire:

  • Target Head: Autoregressive Temporal Modeling

    • Representative

  • Feature Backbone: Joint Feature Extraction and Interaction

    • Advantage

      • Benefit from pre-trained vision Transformer models.
      • Free from randomly initialized correlation modules.
      • More discriminative target-specific feature extraction.
      • Much faster inference and training convergence speed.
      • Simple and generic one-branch tracking framework.
    • Roadmap

      • 1st step :feet: feature interaction inside the backbone.
      • 2nd step :feet: concatenation-based feature interaction.
      • 3rd step :feet: joint feature extraction and interaction.
      • 4th step :feet: generalized and robust relation modeling.

:bookmark:Unified Tracking (UT)

CVPR 2024

  • GLEE (General Object Foundation Model for Images and Videos at Scale) [paper] [code]
  • OmniViD (OmniVid: A Generative Framework for Universal Video Understanding) [paper] [code]

CVPR 2023

  • OmniTracker (OmniTracker: Unifying Object Tracking by Tracking-with-Detection) [paper] [code]
  • UNINEXT (Universal Instance Perception as Object Discovery and Retrieval) [paper] [code]

ICCV 2023

  • MITS (Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation) [paper] [code]

Preprint 2023

  • HQTrack (Tracking Anything in High Quality) [paper] [code]
  • SAM-Track (Segment and Track Anything) [paper] [code]
  • TAM (Track Anything: Segment Anything Meets Videos) [paper] [code]

CVPR 2022

  • UTT (Unified Transformer Tracker for Object Tracking) [paper] [code]

ECCV 2022

  • Unicorn (Towards Grand Unification of Object Tracking) [paper] [code]

:bookmark:Single Object Tracking (SOT)

CVPR 2024

  • AQATrack (Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers) [paper] [code]
  • ARTrackV2 (ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe) [paper] [code]
  • DiffusionTrack (DiffusionTrack: Point Set Diffusion Model for Visual Object Tracking) [paper] [code]
  • HDETrack (Event Stream-Based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline) [paper] [code]
  • HIPTrack (HIPTrack: Visual Tracking with Historical Prompts) [paper] [code]
  • OneTracker (OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning) [paper] [code]
  • QueryNLT (Context-Aware Integration of Language and Visual References for Natural Language Tracking) [paper] [code]
  • SDSTrack (SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking) [paper] [code]
  • Un-Track (Single-Model and Any-Modality for Video Object Tracking) [paper] [code]

ECCV 2024

  • Diff-Tracker (Diff-Tracker: Text-to-Image Diffusion Models are Unsupervised Trackers) [paper] [code]
  • LoRAT (Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance) [paper] [code]

AAAI 2024

  • BAT (Bi-Directional Adapter for Multi-Modal Tracking) [paper] [code]
  • EVPTrack (Explicit Visual Prompts for Visual Object Tracking) [paper] [code]
  • ODTrack (ODTrack: Online Dense Temporal Token Learning for Visual Tracking) [paper] [code]
  • STCFormer (Sequential Fusion Based Multi-Granularity Consistency for Space-Time Transformer Tracking) [paper] [code]
  • TATrack (Temporal Adaptive RGBT Tracking with Modality Prompt) [paper] [code]
  • UVLTrack (Unifying Visual and Vision-Language Tracking via Contrastive Learning) [paper] [code]

ICML 2024

  • AVTrack (Learning Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking) [paper] [code]

IJCAI 2024

  • USTrack (Unified Single-Stage Transformer Network for Efficient RGB-T Tracking) [paper] [code]

WACV 2024

  • SMAT (Separable Self and Mixed Attention Transformers for Efficient Object Tracking) [paper] [code]
  • TaMOs (Beyond SOT: It's Time to Track Multiple Generic Objects at Once) [paper] [code]

ICRA 2024

  • DCPT (DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs) [paper] [code]

Preprint 2024

  • ABTrack (Adaptively Bypassing Vision Transformer Blocks for Efficient Visual Tracking) [paper] [code]
  • ACTrack (ACTrack: Adding Spatio-Temporal Condition for Visual Object Tracking) [paper] [code]
  • AFter (AFter: Attention-Based Fusion Router for RGBT Tracking) [paper] [code]
  • AMTTrack (Long-Term Frame-Event Visual Tracking: Benchmark Dataset and Baseline) [paper] [code]
  • BofN (Predicting the Best of N Visual Trackers) [paper] [code]
  • CAFormer (Cross-modulated Attention Transformer for RGBT Tracking) [paper] [code]
  • CRSOT (CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras) [paper] [code]
  • CSTNet (Transformer-Based RGB-T Tracking with Channel and Spatial Feature Fusion) [paper] [code]
  • DyTrack (Exploring Dynamic Transformer for Efficient Object Tracking) [paper] [code]
  • eMoE-Tracker (eMoE-Tracker: Environmental MoE-Based Transformer for Robust Event-Guided Object Tracking) [paper] [code]
  • LoReTrack (LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking) [paper] [code]
  • MAPNet (Multi-Attention Associate Prediction Network for Visual Tracking) [paper] [code]
  • MDETrack (Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning) [paper] [code]
  • MMMP (From Two Stream to One Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation) [paper] [code]
  • M3PT (Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking) [paper] [code]
  • NLMTrack (Enhancing Thermal Infrared Tracking with Natural Language Modeling and Coordinate Sequence Generation) [paper] [code]
  • OIFTrack (Optimized Information Flow for Transformer

编辑推荐精选

AEE

AEE

AI Excel全自动制表工具

AEE 在线 AI 全自动 Excel 编辑器,提供智能录入、自动公式、数据整理、图表生成等功能,高效处理 Excel 任务,提升办公效率。支持自动高亮数据、批量计算、不规则数据录入,适用于企业、教育、金融等多场景。

UI-TARS-desktop

UI-TARS-desktop

基于 UI-TARS 视觉语言模型的桌面应用,可通过自然语言控制计算机进行多模态操作。

UI-TARS-desktop 是一款功能强大的桌面应用,基于 UI-TARS(视觉语言模型)构建。它具备自然语言控制、截图与视觉识别、精确的鼠标键盘控制等功能,支持跨平台使用(Windows/MacOS),能提供实时反馈和状态显示,且数据完全本地处理,保障隐私安全。该应用集成了多种大语言模型和搜索方式,还可进行文件系统操作。适用于需要智能交互和自动化任务的场景,如信息检索、文件管理等。其提供了详细的文档,包括快速启动、部署、贡献指南和 SDK 使用说明等,方便开发者使用和扩展。

Wan2.1

Wan2.1

开源且先进的大规模视频生成模型项目

Wan2.1 是一个开源且先进的大规模视频生成模型项目,支持文本到图像、文本到视频、图像到视频等多种生成任务。它具备丰富的配置选项,可调整分辨率、扩散步数等参数,还能对提示词进行增强。使用了多种先进技术和工具,在视频和图像生成领域具有广泛应用前景,适合研究人员和开发者使用。

爱图表

爱图表

全流程 AI 驱动的数据可视化工具,助力用户轻松创作高颜值图表

爱图表(aitubiao.com)就是AI图表,是由镝数科技推出的一款创新型智能数据可视化平台,专注于为用户提供便捷的图表生成、数据分析和报告撰写服务。爱图表是中国首个在图表场景接入DeepSeek的产品。通过接入前沿的DeepSeek系列AI模型,爱图表结合强大的数据处理能力与智能化功能,致力于帮助职场人士高效处理和表达数据,提升工作效率和报告质量。

Qwen2.5-VL

Qwen2.5-VL

一款强大的视觉语言模型,支持图像和视频输入

Qwen2.5-VL 是一款强大的视觉语言模型,支持图像和视频输入,可用于多种场景,如商品特点总结、图像文字识别等。项目提供了 OpenAI API 服务、Web UI 示例等部署方式,还包含了视觉处理工具,有助于开发者快速集成和使用,提升工作效率。

HunyuanVideo

HunyuanVideo

HunyuanVideo 是一个可基于文本生成高质量图像和视频的项目。

HunyuanVideo 是一个专注于文本到图像及视频生成的项目。它具备强大的视频生成能力,支持多种分辨率和视频长度选择,能根据用户输入的文本生成逼真的图像和视频。使用先进的技术架构和算法,可灵活调整生成参数,满足不同场景的需求,是文本生成图像视频领域的优质工具。

WebUI for Browser Use

WebUI for Browser Use

一个基于 Gradio 构建的 WebUI,支持与浏览器智能体进行便捷交互。

WebUI for Browser Use 是一个强大的项目,它集成了多种大型语言模型,支持自定义浏览器使用,具备持久化浏览器会话等功能。用户可以通过简洁友好的界面轻松控制浏览器智能体完成各类任务,无论是数据提取、网页导航还是表单填写等操作都能高效实现,有利于提高工作效率和获取信息的便捷性。该项目适合开发者、研究人员以及需要自动化浏览器操作的人群使用,在 SEO 优化方面,其关键词涵盖浏览器使用、WebUI、大型语言模型集成等,有助于提高网页在搜索引擎中的曝光度。

xiaozhi-esp32

xiaozhi-esp32

基于 ESP32 的小智 AI 开发项目,支持多种网络连接与协议,实现语音交互等功能。

xiaozhi-esp32 是一个极具创新性的基于 ESP32 的开发项目,专注于人工智能语音交互领域。项目涵盖了丰富的功能,如网络连接、OTA 升级、设备激活等,同时支持多种语言。无论是开发爱好者还是专业开发者,都能借助该项目快速搭建起高效的 AI 语音交互系统,为智能设备开发提供强大助力。

olmocr

olmocr

一个用于 OCR 的项目,支持多种模型和服务器进行 PDF 到 Markdown 的转换,并提供测试和报告功能。

olmocr 是一个专注于光学字符识别(OCR)的 Python 项目,由 Allen Institute for Artificial Intelligence 开发。它支持多种模型和服务器,如 vllm、sglang、OpenAI 等,可将 PDF 文件的页面转换为 Markdown 格式。项目还提供了测试框架和 HTML 报告生成功能,方便用户对 OCR 结果进行评估和分析。适用于科研、文档处理等领域,有助于提高工作效率和准确性。

飞书多维表格

飞书多维表格

飞书多维表格 ×DeepSeek R1 满血版

飞书多维表格联合 DeepSeek R1 模型,提供 AI 自动化解决方案,支持批量写作、数据分析、跨模态处理等功能,适用于电商、短视频、影视创作等场景,提升企业生产力与创作效率。关键词:飞书多维表格、DeepSeek R1、AI 自动化、批量处理、企业协同工具。

下拉加载更多