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..
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking
Authors: Xinyu Zhou, Jinglun Li, Lingyi Hong, Kaixun Jiang, Pinxue Guo, Weifeng Ge, Wenqiang Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. |
| Researcher Affiliation | Academia | 1Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China 2Shanghai Engineering Research Center of AI & Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks clearly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | We will provide the code and model after the paper be accepted |
| Open Datasets | Yes | The model is trained on full dataests (COCO, GOT-10k, Tracking Net, and La SOT). |
| Dataset Splits | No | The paper mentions training on specific datasets and a testing phase, but does not explicitly provide specific details about training/validation/test dataset splits (e.g., percentages, sample counts for validation). |
| Hardware Specification | Yes | Our experiments are conducted on Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz with 252GB RAM and 8 NVIDIA Ge Force RTX 3090 GPUs with 24GB memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | A total of 240 epochs are trained, with the learning rate set to 8e-5 for the denoising Vi T and 8e-6 for the box refining. The learning rate decreases by a factor of 10 at the 192-th epoch. |