Semantically labeling input followed by discriminating different instances using clustering approaches is an effective strategy for tasks that require understanding both the semantic content (e.g., recognizing what objects are present) and the instance-level differentiation (e.g., identifying each unique occurrence of those objects). This is often seen in instance segmentation, object tracking, and scene understanding in computer vision.

1. Semantic Labeling:

Semantic labeling (or segmentation) assigns a class label to each pixel or region in an input, meaning it identifies and classifies parts of the image into predefined categories (e.g., distinguishing background from objects like "car," "tree," etc.). However, semantic segmentation alone does not differentiate between separate instances of the same class.

Techniques for Semantic Labeling:

• Convolutional Neural Networks (CNNs): Architectures like U-Net, DeepLab, and FCN (Fully Convolutional Networks) are popular for semantic segmentation.
• Transformers: Vision transformers (ViTs) and hybrid CNN-transformer models are becoming increasingly common for this purpose.

2. Discriminating Different Instances:

To go beyond semantic labeling and differentiate between separate instances of the same object class, instance segmentation or object detection models are used. One effective method is to apply clustering approaches to distinguish these instances.

Approaches for Instance Differentiation:

1. Embedding-Based Clustering:
   • Models output an embedding vector for each pixel that encodes its identity. Points in the embedding space that belong to the same object instance are closer to each other than to points from different instances.
   • Clustering algorithms like k-means, DBSCAN, or mean-shift can then be used to group pixels into distinct instances.
2. Distance Metrics:
   • Pixels or features that are close together in the embedding space are considered part of the same instance.
   • The use of learned distance metrics allows the model to generalize well and group pixels effectively.
3. Watershed Algorithm:
   • Applied on top of the output of semantic segmentation to identify the boundaries of different instances.
   • Used to segment continuous regions of an image by treating pixel intensity as a topographic surface and "flooding" the surface from local minima to identify separate basins (instances).
4. CenterNet and Similar Models:
   • Predicts the center of each object instance and uses a clustering approach to group pixels or regions around these centers.

3. Combining Semantic Segmentation with Clustering:

A typical workflow could involve:

• Step 1: Semantic segmentation of the input using a model that outputs per-pixel class labels.
• Step 2: Extraction of features or embeddings from the segmentation map or intermediate layers.
• Step 3: Application of clustering to the extracted features to separate them into unique instances.

Advantages:

• Flexibility: Clustering methods can handle various numbers of instances dynamically.