SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Understand

We present SegNeXt, a simple convolutional network architecture for semantic segmentation.

• Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information.
• In this paper, we show that convolutional attention is a more efficient and effective way to encode contextual information than the self-attention mechanism in transformers.
• By re-examining the characteristics owned by successful segmentation models, we discover several key components leading to the performance improvement of segmentation models.