Visual recognition give answers to the task of explaining the content of an image in terms of “What is it?” and “where is it?”. The answer to these questions is usally a class label corresponding to the object or object types in the image, a tight bounding box containing the object in question, or, at a finer level, the region (pixels) that is its outline. These tasks are called, respectively, image classification, object detection and semantic segmentation. A question is “give me objects like this one”, that requires learning a similary metric between images, even in the case come from different modalities, like sketches and photographs, through the so called encoder-decoder architectures. VR module covers neural network architectures addressing these four types of tasks. And, as a practical complement, methods to implement them.
Specifically, in this module we give to the student an overview of the latest methods based on deep learning techniques to solve visual recognition problems. The final aim is the understanding of complex scenes to build feasible systems for automatic image understanding able to answer the complex question of what objects and where are these objects in a complex scene.
Having addressed the task of classification in module M2, the students will learn a large family of successful architectures of deep convolutional networks that have been proved to solve the visual tasks of detection and segmentation and recognition. In addition to these two visual tasks, this module addresses also advanced topics in deep learning such as architectures for image generation (GANs and VAEs) plus encoder-decoder architectures for multimodal applications.
Module Project: Deep Learning for Classification, Detection and Segmentation
The aiml of the project is to acquint the students with several deep learning architectures for detection, segmentation and recognition of objects in the context of image understanding. The project will focus on the application of object detection and segmentation to autonomous driving with the detection of cars and pedestrians. During the project different architectures will be trained, tested, evaluated and compared.
M5 Schedule – Academic Year 2021-2022 – Student Guide <here>