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Found 7 papers, 2 papers with code
AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows
Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities.
Exploring vision transformer layer choosing for semantic segmentation
We validate the effectiveness of our method on different datasets and models and surpass previous state-of-the-art methods.
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation
We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation.
UniNeXt: Exploring A Unified Architecture for Vision Recognition
Interestingly, the ranking of these spatial token mixers also changes under our UniNeXt, suggesting that an excellent spatial token mixer may be stifled due to a suboptimal general architecture, which further shows the importance of the study on the general architecture of vision backbone.
Feature Selective Transformer for Semantic Image Segmentation
In this work, we focus on fusing multi-scale features from Transformer-based backbones for semantic segmentation, and propose a Feature Selective Transformer (FeSeFormer), which aggregates features from all scales (or levels) for each query feature.
StructToken : Rethinking Semantic Segmentation with Structural Prior
In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, \textit{i. e.,} classify each pixel representation to a specific category.
Fully Transformer Networks for Semantic Image Segmentation
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies.
