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 [1].
Self-Supervised Multi-Object Tracking with Cross-input Consistency
Authors: Favyen Bastani, Songtao He, Samuel Madden
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our unsupervised method on MOT17 and KITTI remarkably, we ο¬nd that, despite training only on unlabeled video, our unsupervised approach outperforms four supervised methods published in the last 1β2 years, including Tracktor++ [1], FAMNet [5], GSM [18], and mm MOT [29]. |
| Researcher Affiliation | Academia | Favyen Bastani MIT CSAIL EMAIL Songtao He MIT CSAIL EMAIL Sam Madden MIT CSAIL EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://favyen.com/uns20/. |
| Open Datasets | Yes | We evaluate our approach on the MOT17 and KITTI benchmarks against 9 baselines... For MOT17, we collect unlabeled video from two sources: we use ο¬ve hours of video from seven You Tube walking tours, and all train and test sequences from the Path Track dataset [20] (we do not use the Path Track ground truth annotations). For KITTI, we use both the 46 minutes of video in the KITTI dataset together with 7 hours of video from Berkeley Deep Drive [27]. |
| Dataset Splits | No | The paper specifies training and testing splits for MOT17 and KITTI datasets, but it does not explicitly provide information about a validation dataset split (e.g., percentages or counts). |
| Hardware Specification | Yes | We train our tracker model on an NVIDIA Tesla V100 GPU; training time varies between 4 and 24 hours depending on the input-hiding scheme. |
| Software Dependencies | No | The paper mentions using a YOLOv5 model and the Adam optimizer but does not provide specific version numbers for these or other software dependencies, such as programming languages or libraries. |
| Experiment Setup | Yes | During training, we randomly select sequence lengths n between 4 and 16 frames, and apply stochastic gradient descent one sequence at a time. We apply the Adam optimizer with learning rate 0.0001, decaying to 0.00001 after plateau. |