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..
Compact Transformer Tracker with Correlative Masked Modeling
Authors: Zikai Song, Run Luo, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the proposed compact transform tracker outperforms existing approaches, including advanced attention variants, and demonstrates the sufficiency of self-attention in tracking tasks. Our method achieves state-of-the-art performance on five challenging datasets, along with the VOT2020, UAV123, La SOT, Tracking Net, and GOT-10k benchmarks. |
| Researcher Affiliation | Academia | Zikai Song1, Run Luo1, Junqing Yu1 , Yi-Ping Phoebe Chen2, Wei Yang1 1Huazhong University of Science and Technology, China 2La Trobe University, Australia |
| Pseudocode | No | The paper describes its approach verbally and with figures but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our project is available at https://github.com/HUSTDML/CTTrack. |
| Open Datasets | Yes | We adopt Co Co(Lin et al. 2014), La SOT(Fan et al. 2019), GOT-10k(Huang, Zhao, and Huang 2019), and Tracking Net(Muller et al. 2018) as our training dataset except the GOT-10k benchmark. |
| Dataset Splits | No | The paper mentions training on various datasets and an ablation study on La SOT as a validation accuracy measure, but it does not provide explicit percentages or sample counts for training/validation/test splits needed to reproduce the data partitioning. |
| Hardware Specification | Yes | We train our model on 4 Nvidia Tesla V100 GPUs for a total of 500 epochs, each epoch uses 6 104 images. |
| Software Dependencies | Yes | Our approach is implemented in Python 3.7 with Py Torch 1.7. |
| Experiment Setup | Yes | The Adam W optimizer (Loshchilov and Hutter 2018) is employed with initial learning rate (lr) of 1e-4 with the layer-wise decay 0.75, and the lr decreases according to the cosine function with the final decrease factor of 0.1. We adopt a warm-up lr with the 0.2 warm-up factor on the first 5 epochs. We train our model on 4 Nvidia Tesla V100 GPUs for a total of 500 epochs, each epoch uses 6 104 images. The mini-batch size is set to 128 images with each GPU hosting 32 images. |