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..
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. |