Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Discriminative and Robust Online Learning for Siamese Visual Tracking
Authors: Jinghao Zhou, Peng Wang, Haoyang Sun13017-13024
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments4.2 Comparison with state-of-the-art4.3 Ablation StudyTable 1: state-of-the-art comparison on two popular tracking benchmarks OTB2015 and VOT2018 with their running speed. |
| Researcher Affiliation | Academia | Jinghao Zhou, Peng Wang, Haoyang Sun School of Computer Science and School of Automation, Northwestern Polytechnical University, China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Tracking algorithm |
| Open Source Code | Yes | Our method is implemented in Python with Py Torch, and the complete code and video demo will be made available at https://github.com/shallowtoil/DROL. |
| Open Datasets | Yes | OTB100, VOT2018, VOT2018-LT, UAV123, Tracking Net, and La SOT.OTB2015 (Wu, Lim, and Yang 2015)VOT2018 (Kristan et al. 2018) |
| Dataset Splits | Yes | The above hyper-parameters are set using VOT2018 as the validation set and are further evaluated in Section 5. |
| Hardware Specification | Yes | The speed is tested on Nvidia GTX 1080Ti GPU. |
| Software Dependencies | No | Our method is implemented in Python with Py Torch, and the complete code and video demo will be made available at https://github.com/shallowtoil/DROL. The paper mentions Python and PyTorch but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For the classification subnet, the first layer is a 1 1 convolutional layer with Re LU activation, which reduces the feature dimensionality to 64. The last layer employs a 4 4 kernel with a single output channel. (...) For online tuning, we use the region of size 255 255 of the first frame to pre-train the whole classifier. (...) The classifier is updated every 10 frame with a learning rate set to 0.01 and doubled once neighboured distractors are detected. To fuse classification scores, we set λ to 0.6 in DROL-FC and 0.8 in DROL-RPN and DROL-Mask. (...) we update the short-term template every T = 5 frames, while τc, υr, and υc are set to 0.75, 0.6, and 0.5 respectively. |