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..
Unsupervised Embedding and Association Network for Multi-Object Tracking
Authors: Yu-Lei Li
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that UEANet confirms the outstanding ability to suppress IDS and achieves comparable performance compared with state-of-the-art methods on three MOT datasets. |
| Researcher Affiliation | Academia | Yu-Lei Li School of Informatics, Xiamen University EMAIL |
| Pseudocode | No | The paper describes the methods using text and mathematical formulations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate the proposed UEANet on the MOT2016 [Milan et al., 2016], MOT2017 [Milan et al., 2016] and MOT2020 [Dendorfer et al., 2020] datasets. |
| Dataset Splits | Yes | For ablation studies in Section 4.2, we follow the previous methods [Zhang et al., 2021b; Wang et al., 2021b; Wu et al., 2021] to use the first half of each video sequence of the MOT2017 training set for training while using the second half for validation. |
| Hardware Specification | Yes | We train the backbone [Zhang et al., 2021b], and the detection, identity embedding and data association branches of UEANet for 30 epochs with a learning rate of 1 × 10−4 and a mini-batch size of 24 on 4 RTX2080 Ti GPUs (using 2 RTX2080 Ti GPUs for ablation studies). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific versions of Python, PyTorch, or TensorFlow). |
| Experiment Setup | Yes | We train the backbone [Zhang et al., 2021b], and the detection, identity embedding and data association branches of UEANet for 30 epochs with a learning rate of 1 × 10−4 and a mini-batch size of 24 on 4 RTX2080 Ti GPUs (using 2 RTX2080 Ti GPUs for ablation studies). ... The association confidence threshold β is 0.4 during inference. |