Transformers were introduced in 2017 and changed the course of Deep Learning research for many forthcoming years. Naturally, they were also adopted in the computer vision ecosystem which was till then, dominated by Convolutional Neural Networks or Co...
Machine Learning frameworks make our lives easier by offering built-in methods for faster prototyping and optimized GPU training. They also help by providing robust and scalable solutions which help engineers implement state-of-the-art models in a be...
Remember how we used SVD to extract features from an image which helped our machine “see”? Today we are going to discuss one more such method. HOG (Histogram of Oriented Gradients) is a feature descriptor that captures the shape and structure of obje...
Many real-world datasets are corrupted by noise and outliers. Traditional methods for model fitting, such as least squares, are highly susceptible to these outliers, as even a single outlier can significantly distort the estimated parameters. Techniq...
In 3D Computer Vision, camera calibration is the process of determining specific camera parameters to complete operations with specified performance measures. These parameters include focal length, optical centre, skew coefficient, radian and tangent...
Since the forward pass ended on the Fully Connected Layer, it is only natural to begin the discussion of backpropagation from that layer. Fortunately for us, it is not any different than what we usually see in regular Neural Networks. Therefore, we c...
