mlsanityData to 7 Oct 2026

Fully-Convolutional Siamese Networks for Object Tracking

arXiv 1606.09549 ↗First posted 30 Jun 2016cs.CVcited by 566

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Abstract (arXiv)

The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself. Despite the success of these methods, their online-only approach inherently limits the richness of the model they can learn. Recently, several attempts have been made to exploit the expressive power of deep convolutional networks. However, when the object to track is not known beforehand, it is necessary to perform Stochastic Gradient Descent online to adapt the weights of the network, severely compromising the speed of the system. In this paper we equip a basic tracking algorithm with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video. Our tracker operates at frame-rates beyond real-time and, despite its extreme simplicity, achieves state-of-the-art performance in multiple benchmarks.

Key facts. Cited by 566 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jun 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 145; 2022: 120; 2023: 96; 2024: 97; 2025: 66; 2026: 41.

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  1. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  2. 2023CoTracker: It is Better to Track Togethercited by 516
  3. 2021Transformer Trackingcited by 412
  4. 2021Learning Spatio-Temporal Transformer for Visual Trackingcited by 348
  5. 2022Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Frameworkcited by 328
  6. 2023Perception Test: A Diagnostic Benchmark for Multimodal Video Modelscited by 310
  7. 2022MixFormer: End-to-End Tracking with Iterative Mixed Attentioncited by 278
  8. 2021Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Trackingcited by 220
  9. 2025InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modelingcited by 203
  10. 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 200
  11. 2023Universal Instance Perception as Object Discovery and Retrievalcited by 190
  12. 2021PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clusteringcited by 163
38 more
  1. 2023Visual Prompt Multi-Modal Trackingcited by 141
  2. 2022AiATrack: Attention in Attention for Transformer Visual Trackingcited by 137
  3. 2021Learning Target Candidate Association to Keep Track of What Not to Trackcited by 131
  4. 2021VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flowscited by 128
  5. 2021SwinTrack: A Simple and Strong Baseline for Transformer Trackingcited by 126
  6. 2021Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmarkcited by 124
  7. 2022Backbone is All Your Need: A Simplified Architecture for Visual Object Trackingcited by 119
  8. 2024One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videoscited by 99
  9. 2021Locate then Segment: A Strong Pipeline for Referring Image Segmentationcited by 98
  10. 2022Towards Grand Unification of Object Trackingcited by 93
  11. 2023Generalized Relation Modeling for Transformer Trackingcited by 93
  12. 2021STMTrack: Template-free Visual Tracking with Space-time Memory Networkscited by 91
  13. 2022Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baselinecited by 90
  14. 2021Object Tracking by Jointly Exploiting Frame and Event Domaincited by 83
  15. 2021LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Searchcited by 82
  16. 2021Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspectivecited by 81
  17. 2021Monocular Quasi-Dense 3D Object Trackingcited by 80
  18. 2022Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Countingcited by 80
  19. 2023Robust Object Modeling for Visual Trackingcited by 80
  20. 2024Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformerscited by 76
  21. 2022Transformer Tracking with Cyclic Shifting Window Attentioncited by 75
  22. 2023Siamese Masked Autoencoderscited by 75
  23. 2023Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baselinecited by 73
  24. 2021HiFT: Hierarchical Feature Transformer for Aerial Trackingcited by 72
  25. 2023HIPTrack: Visual Tracking with Historical Promptscited by 71
  26. 2024Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performancecited by 70
  27. 2022TCTrack: Temporal Contexts for Aerial Trackingcited by 69
  28. 2021Multiple Object Tracking with Correlation Learningcited by 67
  29. 2021Learn to Match: Automatic Matching Network Design for Visual Trackingcited by 67
  30. 2021Box-Aware Feature Enhancement for Single Object Tracking on Point Cloudscited by 66
  31. 2021DepthTrack : Unveiling the Power of RGBD Trackingcited by 66
  32. 2021PTTR: Relational 3D Point Cloud Object Tracking with Transformercited by 65
  33. 2022A Low-Shot Object Counting Network With Iterative Prototype Adaptationcited by 65
  34. 2024OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuningcited by 64
  35. 2021Learning Position and Target Consistency for Memory-based Video Object Segmentationcited by 63
  36. 2024A Distractor-Aware Memory for Visual Object Tracking with SAM2cited by 63
  37. 2021SiamMOT: Siamese Multi-Object Trackingcited by 62
  38. 2021Do Different Tracking Tasks Require Different Appearance Models?cited by 62

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