Vision transformer for image segmentation

Question

I am working with vision transformers (ViT) for the task of image segmentation, but I am unsure of which segmentation head to use.

I know I need a vision transformer as my backbone, and a segmentation head to generate the image segmentation from the learned representations of the backbone given an input image. Can I use any segmentation head with a ViT backbone, or do certain heads apply to specific ViT backbones?

Appreciate anyone who can offer some insight!

Answer 1

Understanding segmentation heads: They are essentially the final layers of your model responsible for translating the rich feature representations from the backbone(ViT in this case) into pixel-level class predictions (i.e., the segmentation mask). Several popular types of segmentation heads exist, each with its strengths and considerations:

Common Segmentation Heads:

1. Linear decoder: This is the simplest type of head, where the flattened output of the ViT backbone is fed into a linear layer (or a few) to predict class probabilities for each pixel. It's computationally efficient but might lack the ability to capture fine-grained details.
2. MLP Decoders: Similar to the linear decoder, but with multiple layers and potentially non-linearities to allow for more complex feature transformations.
3. CNNs: To implement, you need to reshape and interpolate the token outputs into a 2D grid before applying convolutional layers.
4. U-Net like decoder: Inspired by the popular U-Net architecture, these decoders have skip connections that combine low-level (detail-rich) features from the backbone with high-level (semantic-rich) features from the decoder. Transformer tokens can be reshaped and processed through a U-Net-like decoder with skip connections. Check out TransUNet as mention below list of implementations.
5. There are other types of decoder too. I covered most basic once.

The good news is that you're not restricted to specific segmentation heads for specific ViT backbones. In principle, any of the segmentation heads mentioned above can be used with any ViT backbone.

Choosing the Right Head: consider the following factors when choosing a segmentation head:

1. Complexity and Performance: Linear/MLP heads are the simplest but might not offer the best performance. Transformer-based decoders and mask transformers offer better modeling capabilities but can be more complex.

2. Computational Resources: If computational resources are limited, simpler heads like MLP might be preferable.

3. Task-Specific Requirements: consider the nature of the segmentation task. If you need to capture fine details or long-range dependencies, a more powerful decoder might be necessary.

Example implementations with links (Not in specific order):

1. SegFormer: designed to work with transformer backbones.
2. DPT: Uses a ViT backbone followed by a dense prediction head.
3. SegViT: Semantic degmentation with plain vision transformers
4. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
5. TransUNet: Transformers as encoder. U-net like structure.

Answer 2

Segment Anything Model (SAM) - ViT Base (ViT-B)

Segment Anything Model Source Code

Answer 3

For a simple segmentation head, you can use the head of SETR whose backbone is the ViT. And for more advanced choice, you might modify the decoder head yourself. For other transformer-based segmentation models, a specific decoder head is needed. Like SegFormer and Segmenter.