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 | Conference PDF | Archive PDF | Plain Text | 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 (∼17GHz) 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 × 256, 256 × 64 and 256 × 64, respectively... The initial learning rate is 1e−4, and decays in a cosine manner by default. We train these models for 300 epochs with a batch size of 6. |