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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |