(And not limited to the following directions)
Topic 1: Pattern Recognition:
Statistical Pattern Recognition
Structural Pattern Recognition
Fuzzy Pattern Recognition
Bayesian Decision Theory
Support Vector Machine
Hidden Markov Model
Conditional Random Field
Clustering Analysis
Autoencoder and Variational Autoencoder
Contrastive Learning and Self-supervised Learning
Transfer Learning and Domain Adaptation
Meta-Learning and Small Sample Learning Multimodal learning
Topic 2: Image Processing and Underlying Vision
Image Enhancement and Restoration
Image Denoising, Deblurring, and Dehazing
Image Super-Resolution Reconstruction
Image Segmentation
Edge Detection and Contour Extraction
Color and Texture Analysis
Optical Flow and Motion Estimation
Stereo Matching and Depth Estimation
Image Registration
Camera Calibration and Multi-View Geometry
Local Feature Points (SIFT, SURF, ORB)
Directional Gradient Histogram
Bag-of-Words Model and Spatial Pyramid Matching
Visual Salience Detection
Object Detection (Faster R-CNN, YOLO, SSD)
Object Tracking
Instance Segmentation and Panoramic Segmentation
Cross-Direction (Pattern Recognition + Computer Vision)
Deep Learning-driven Visual Recognition
Weak Supervision and Semi-Supervised Visual Learning
Cross-Modal Retrieval (Image-Text)
Application of Generative Models in Vision
Self-Supervised Visual Representation Learning
Domain Generalization and Domain Adaptive Recognition
Application of Federated Learning in Visual Recognition
Interpretable Visual Pattern Analysis
Adversarial Samples and Robust Visual Recognition
Small Sample and Zero Sample Visual Recognition