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..
AdaPKC: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation
Authors: Teng Li, Liwen Zhang, Youcheng Zhang, ZijunHu , Pengcheng Pi, Zongqing Lu, Qingmin Liao, Zhe Ma
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experimental verification using various real-measured radar data (including publicly available low-cost millimeter-wave radar dataset for autonomous driving and self-collected Ku-band surveillance radar dataset), we found that the performance of Ada PKC-based models surpasses other So TA methods in RSS tasks. |
| Researcher Affiliation | Collaboration | 1Intelligent Science and Technology Academy of CASIC 2Shenzhen International Graduate School, Tsinghua University |
| Pseudocode | Yes | Algorithm 1: Voting-driven Multi-round Training |
| Open Source Code | Yes | The code is available at https://github.com/lihua199710/Ada PKC. |
| Open Datasets | Yes | CARRADA [20] dataset is recorded by a low-cost FMCW radar... CARRADA-RAC [32] dataset is derived from CARRADA... Ku RALS dataset is self-collected by a Kurz-under band (ā¼17GHz) surveillance Radar... |
| Dataset Splits | Yes | The dataset splits are the same as in [32, 19]. |
| Hardware Specification | Yes | Frame rate is calculated on a workstation with an Intel(R) Xeon(R) Platinum 8255C CPU and a Tesla V100-SXM2 GPU. ...We train all these models on two NVIDIA-3090 GPUs... |
| Software Dependencies | No | The paper mentions using the Adam optimizer, but does not provide specific version numbers for key software components or libraries like PyTorch, TensorFlow, or Python. |
| Experiment Setup | Yes | The input sizes of RA, AD and RD views are 256 Ć 256, 256 Ć 64 and 256 Ć 64, respectively... The initial learning rate is 1eā4, and decays in a cosine manner by default. We train these models for 300 epochs with a batch size of 6. |