Computer-vision

Published on
June 10, 2024
OpenCV Tutorial: Computer Vision with Python
OpenCV Computer-Vision Python Image-Processing Object-Detection
Comprehensive guide to OpenCV for computer vision applications, covering image processing, feature detection, object tracking, and real-time video analysis with practical examples.
Published on
March 22, 2024
Local Image Descriptors: From SIFT to Learned Features
Computer-Vision SIFT ORB Local-Features Keypoint-Detection
Comprehensive exploration of local image descriptors including classical methods like SIFT and ORB, and modern learned approaches for keypoint detection and description.
Published on
February 28, 2024
Global Image Descriptors: From HOG to Deep Learning Features
Computer-Vision Image-Descriptors Feature-Extraction Deep-Learning
Comprehensive guide to global image descriptors, exploring traditional methods like HOG and LBP alongside modern deep learning approaches for image representation and retrieval.
Published on
January 25, 2024
ConvNeXt: How Classic CNNs Fought Back Against Transformers
ConvNeXt Computer-Vision CNN-Architecture Deep-Learning Design-Principles
Understanding ConvNeXt - the systematic modernization of convolutional networks that proved CNNs could compete with Vision Transformers by adopting their best design principles.
Published on
December 18, 2023
Test-Time Augmentation (TTA): Boosting Model Performance at Inference
Test-Time-Augmentation Computer-Vision Model-Inference PyTorch
Exploring Test-Time Augmentation techniques that improve model predictions by aggregating results from multiple augmented versions of test images.
Published on
November 16, 2023
Image Augmentation: The Art of Creating More from Less
Computer-Vision Data-Augmentation Deep-Learning Machine-Learning Image-Processing
Understanding image augmentation - why it's crucial for computer vision success, what techniques work best, and how to apply them effectively to build robust models with limited data.
Published on
October 14, 2023
Contrastive Learning: Teaching AI Through Comparison
Contrastive-Learning Self-Supervised-Learning Computer-Vision Representation-Learning
Understanding contrastive learning - the breakthrough approach that teaches AI to recognize patterns by comparing what's similar and what's different, without needing labeled data.
Published on
May 12, 2023
Stable Diffusion: How AI Learned to Paint from Pure Noise
Stable-Diffusion Generative-AI Computer-Vision Diffusion-Models
Understanding Stable Diffusion - the breakthrough AI system that can create stunning images from text by learning to reverse the process of adding noise to pictures.
Published on
April 20, 2023
Vision Transformer (ViT): Bringing Transformers to Computer Vision
Computer-Vision Transformers Image-Classification AI-Fundamentals
How Vision Transformers challenged CNNs by treating images like sentences - breaking them into patches and using attention to understand spatial relationships.
Published on
February 19, 2023
Computer Vision Tasks: From Object Detection to Panoptic Segmentation
Computer-Vision Object-Detection Segmentation Deep-Learning Image-Analysis
Understanding the different computer vision tasks - object detection, semantic segmentation, instance segmentation, and panoptic segmentation - their applications, and when to use each approach.
Published on
February 12, 2023
CLIP: Teaching AI to Connect Images and Language
CLIP Multimodal-AI Computer-Vision NLP Zero-shot-Learning
Understanding CLIP - the breakthrough model that bridges vision and language, enabling AI to understand images through natural language without task-specific training.
Published on
January 24, 2023
ArcFace Loss: Teaching Neural Networks to Create Perfect Boundaries
Python pytorch loss-functions computer-vision face-recognition
Understanding ArcFace loss - the breakthrough technique that revolutionized face recognition by teaching networks to create better feature boundaries through angular margins.