First, let me explain what semantic segmentation is it’s a process where we take an image and assign each pixel to a specific category (like road or building). Now, imagine you have a bunch of images with different categories, but you want your model to learn how to classify new categories without forgetting the old ones. That’s what incremental semantic segmentation is all about!

So, this paper proposes a method called “A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images”. Basically, they use contrastive learning (which is like comparing two things to see if they’re similar or different) and knowledge distillation (which involves transferring knowledge from a teacher model to a student model). They apply this method specifically to semantic segmentation in aerial images.

Here’s how it works: first, you have your original dataset with labeled categories for each pixel. Then, you train a teacher model on this data and get its output feature maps (which are basically the results of running the input image through the neural network). Next, you take these feature maps and divide them into smaller partitions (like dividing an apple into slices). These partitions become your contrastive samples.

Now, for each partition in the teacher’s feature map, you find a corresponding partition in the student’s feature map that represents the same category. You then compare these two partitions to see if they’re similar or different (using contrastive learning). If they’re similar, you transfer some of the knowledge from the teacher’s partition to the student’s partition using knowledge distillation.

This process helps the student model learn how to classify new categories without forgetting the old ones because it’s building on top of what the teacher already knows. And since we’re not doing any data augmentation or storing feature maps in memory buffer, this method is more efficient than some other contrastive distillation methods out there!

So, to summarize: “A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images” uses contrastive learning and knowledge distillation to help a student model learn how to classify new categories without forgetting the old ones. It does this by comparing partitions of feature maps from both the teacher and student models, and transferring some of the knowledge from the teacher’s partition to the student’s partition if they’re similar. This method is more efficient than other contrastive distillation methods because it doesn’t require data augmentation or storing feature maps in memory buffer!