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..
Learning Patch-Based Dynamic Graph for Visual Tracking
Authors: Chenglong Li, Liang Lin, Wangmeng Zuo, Jin Tang
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our approach outperforms the state-of-the-art tracking methods on two standard benchmarks, i.e., OTB100 and NUS-PRO. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Anhui University, Hefei,China 2School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China 3School of Computer Science and Technology, Harbin Institute of Technology, China |
| Pseudocode | Yes | Alg. 1 summarizes the optimization procedure. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the proposed tracking method on the OTB100 benchmark dataset (Wu, Lim, and Yang 2015). [...] We also compare our approach with other tracking approaches on another large-scale benchmark dataset, NUS-PRO (Li et al. 2016a). |
| Dataset Splits | No | The paper mentions using OTB100 and NUS-PRO datasets for evaluation but does not specify explicit training, validation, and test splits with percentages, sample counts, or references to predefined splits for reproducibility. |
| Hardware Specification | Yes | The experiments are carried out on a PC with an Intel i7 4.0GHz CPU and 32GB RAM, and implemented in C++. |
| Software Dependencies | No | The paper states the implementation is "in C++" but does not provide specific version numbers for C++ compilers or any other libraries/dependencies used. |
| Experiment Setup | Yes | In Eq. (2), we empirically set {α, λ, β, γ, ξ} = {1, 0.1, 5, 18, 1}. In Struck, we empirically set {ω, θ} = {0.67, 0.2}. Besides, we partition all bounding box into 64 non-overlapping patches [...] each frame is scaled so that the minimum side length of a bounding box is 32 pixels, and the side length of a searching window is fixed to be 2 WH. |