1. Scale-Invariant Feature Transform (SIFT)
2. Speeded Up Robust Features (SURF)
3. Histogram of Oriented Gradients (HOG)
4. Support Vector Machine (SVM)
5. Random Forest (RF)
6. Adaboost
7. K-Nearest Neighbors (KNN)
8. K-Means Clustering

While these techniques might be relevant for learning purposes, they are not used in the current state-of-the-art methods for image understanding. The following techniques are more relevant for the current state-of-the-art methods.

1. Convolutional Neural Networks (CNN)
   • ResNet
   • EfficientNet
2. Region-based CNN (R-CNN)
   • Fast R-CNN
   • Faster R-CNN
   • Mask R-CNN
3. You Only Look Once (YOLO)
   • YOLOv1, ..., YOLOv8
4. Transformer-based
   • Vision Transformer (ViT)
   • Data-efficient Vision Transformer (DeiT)
   • End-to-End Object Detection with Transformers (DETR)
   • Swin Transformer: Hierarchical Vision Transformer
   • Fully Transformer-based Object Detector (ViDT)
   • Point Cloud Transformer (PCT)
5. Vision Permutator: MLP-Like Architecture
6. GAN
7. Diffusion
8. Tracking
   • Key Points
   • CoTracker