  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.
