Delineation: Techniques and Applications

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Covers the techniques for recovering 3D shape information from 2D images using shading models and modern deep learning approaches.

Covers the role of Transformers in unifying various machine learning fields.

Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Covers the fundamentals of computer vision, illusions, challenges, applications, and history.

Emphasizes the need for independent channels when detecting objects in images.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Delves into choosing a proper feature size for image analysis in life sciences, presenting a Rule Of Thumb for defining object size in pixels.

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