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..
RETR: Multi-View Radar Detection Transformer for Indoor Perception
Authors: Ryoma Yataka, Adriano Cardace, Perry Wang, Petros Boufounos, Ryuhei Takahashi
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluated on two indoor radar perception datasets, our approach outperforms existing stateof-the-art methods by a margin of 15.38+ AP for object detection and 11.91+ Io U for instance segmentation, respectively. |
| Researcher Affiliation | Collaboration | 1Mitsubishi Electric Research Laboratories (MERL), USA 2Department of Computer Science and Engineering, University of Bologna, Italy 3Information Technology R&D Center (ITC), Mitsubishi Electric Corporation, Japan |
| Pseudocode | No | The paper describes the architecture and processes in text and diagrams (Figure 3, 7, 8) but does not present them in formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available at https://github.com/merlresearch/radar-detection-transformer. |
| Open Datasets | Yes | We evaluate performance over two open indoor radar perception datasets: MMVR4 [26] and HIBER5 [38]. https://zenodo.org/records/12611978 https://github.com/Intelligent-Perception-Lab/HIBER |
| Dataset Splits | Yes | For the training-validation-test split, we follow the data split S1 as defined in MMVR. Table 5: Details of hyper-parameters. ... # of training 190441 / 118280 # of validation 23899 / 33841 # of test 23458 / 85677 |
| Hardware Specification | Yes | Table 5: Details of hyper-parameters. ... GPU (NVIDIA) A40 |
| Software Dependencies | No | The paper mentions using 'Res Net' as a backbone, but does not specify software dependencies like programming language versions (e.g., Python 3.x) or library versions (e.g., PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | Table 5: Details of hyper-parameters. The table lists specific values for parameters such as 'Total dimension of positional embedding', 'Ratio of depth dimension for TPE', '# of input frames', 'Top-K selection magnitude', '# of encoder blocks', '# of decoder blocks', '# of head of multi-head attention', '# of queries', 'Threshold for detection and segmentation', 'Loss weight for GIo U on horizontal plane', 'Loss weight for L1 on horizontal plane', 'Batch size', 'Epoch for detection', 'Epoch for segmentation', 'Patience for early stopping', 'Learning rate', 'Sheduler', and 'Weight decay'. |