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..
Tracklet Self-Supervised Learning for Unsupervised Person Re-Identification
Authors: Guile Wu, Xiatian Zhu, Shaogang Gong12362-12369
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of TSSL over a wide variety of the stateof-the-art alternative methods on four large-scale person reid benchmarks, including Market-1501, Duke MTMC-Re ID, MARS and Duke MTMC-Video Re ID. |
| Researcher Affiliation | Collaboration | 1Queen Mary University of London, 2Vision Semantics Limited |
| Pseudocode | Yes | Algorithm 1 Tracklet Self-Supervised Learning. |
| 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 aim at optimising a feature embedding space for both image and video unsupervised re-id, so we also evaluated both image (Market-1501 (Zheng et al. 2015) and Duke MTMCRe ID (Ristani et al. 2016; Zheng, Zheng, and Yang 2017)) and video (MARS (Zheng et al. 2016) and Duke MTMCVideo Re ID (Ristani et al. 2016; Wu et al. 2018a)) datasets. |
| Dataset Splits | No | The paper does not explicitly mention a validation split, nor does it detail the percentages or counts for training, validation, and test sets. It mentions a "maximal training epoch" but no specific validation process for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "We used Res Net-50 (He et al. 2016) (pre-trained on Image Net) as the feature embedding network." but does not provide version numbers for ResNet or any other software libraries or dependencies. |
| Experiment Setup | Yes | We empirically set α = 2 for Eq. (4), η = 0.5 for Eq. (6), λ = 0.1 and s = 10 for Eq. (5), τ = 0.1 for Eq. (7), δ = 0.05. We set Nk = 4 for cluster merging. The maximal training epoch was set to 20 for the first step and to 5 for the remaining steps. We used Stochastic Gradient Descent (SGD) as the optimiser with the initial learning rate at 0.01 for the backbone model and a decay of 0.1 after 15 training epochs. |