Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely Sensed Images
<|reference_start|>Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely Sensed Images: Recent advancements in autoregressive networks with linear complexity have driven significant research progress, demonstrating exceptional performance in large language models. A representative model is the Extended Long Short-Term Memory (xLSTM), which incorporates gating mechanisms and memory structures, performing comparably to Transformer architectures in long-sequence language tasks. Autoregressive networks such as xLSTM can utilize image serialization to extend their application to visual tasks such as classification and segmentation. Although existing studies have demonstrated Vision-LSTM's impressive results in image classification, its performance in image semantic segmentation remains unverified. Our study represents the first attempt to evaluate the effectiveness of Vision-LSTM in the semantic segmentation of remotely sensed images. This evaluation is based on a specifically designed encoder-decoder architecture named Seg-LSTM, and comparisons with state-of-the-art segmentation networks. Our study found that Vision-LSTM's performance in semantic segmentation was limited and generally inferior to Vision-Transformers-based and Vision-Mamba-based models in most comparative tests. Future research directions for enhancing Vision-LSTM are recommended. The source code is available from https://github.com/zhuqinfeng1999/Seg-LSTM.<|reference_end|>
@article{zhu2024seg-lstm:,
    title={Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely
  Sensed Images},
    author={Qinfeng Zhu, Yuanzhi Cai, Lei Fan},
    journal={arXiv preprint arXiv:2406.14086},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14086},
    primaryClass={cs.CV cs.AI cs.LG}
}