@inproceedings{
zhang2024tarssnet,
title={{TARSS}-Net: Temporal-Aware Radar Semantic Segmentation Network},
author={Youcheng Zhang and Liwen Zhang and ZijunHu and Pengcheng Pi and Teng Li and Yuanpei Chen and Shi Peng and Zhe Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=5AeLrXb9sQ}
}
Radar signal interpretation plays a crucial role in remote detection and ranging. With the gradual display of the advantages of neural network technology in signal processing, learning-based radar signal interpretation is becoming a research hot-spot and made great progress. And since radar semantic segmentation (RSS) can provide more fine-grained target information, it has become a more concerned direction in this field. However, the temporal information, which is an important clue for analyzing radar data, has not been exploited sufficiently in present RSS frameworks. In this work, we propose a novel temporal information learning paradigm, i.e., data-driven temporal information aggregation with learned target-history relations. Following this idea, a flexible learning module, called Temporal Relation-Aware Module (TRAM) is carefully designed. TRAM contains two main blocks: i) an encoder for capturing the target-history temporal relations (TH-TRE) and ii) a learnable temporal relation attentive pooling (TRAP) for aggregating temporal information. Based on TRAM, an end-to-end Temporal-Aware RSS Network (TARSS-Net) is presented, which has outstanding performance on publicly available and our collected real-measured datasets. Code and supplementary materials are available at https://github.com/zlw9161/TARSS-Net.
TARSS-Net: Temporal-Aware Radar Semantic Segmentation Network
Youcheng Zhang Liwen Zhang ZijunHu Pengcheng Pi Teng Li Yuanpei Chen Shi Peng Zhe Ma