Summary of Seg-lstm: Performance Of Xlstm For Semantic Segmentation Of Remotely Sensed Images, by Qinfeng Zhu et al.
Seg-LSTM: Performance of xLSTM for Semantic Segmentation of Remotely Sensed Images
by Qinfeng Zhu, Yuanzhi Cai, Lei Fan
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an evaluation of the Vision-LSTM model’s performance in semantic segmentation tasks, specifically focusing on remotely sensed images. The authors use a Seg-LSTM encoder-decoder architecture to compare the results with state-of-the-art segmentation networks. The study finds that Vision-LSTM’s performance is limited and generally inferior to other models, such as Vision-Transformers-based and Vision-Mamba-based models. This highlights potential future research directions for enhancing the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a special kind of artificial intelligence called Vision-LSTM. It’s used for tasks like recognizing objects in pictures or dividing an image into different parts based on what it contains. The researchers tested this model to see how well it does when used with satellite images, which have lots of useful information about the Earth. They found that while it can do some things well, it’s not as good as other models at certain tasks. This helps us understand where we should focus our efforts to make the model better. |
Keywords
» Artificial intelligence » Encoder decoder » Lstm » Semantic segmentation
