What are the challenges faced by the computer vision?

Challenge 1: Car sensors and multimodal data. Challenge 2: Gathering representative training data. Challenge 3: Object detection. Challenge 4: Semantic instance segmentation.

Is Deep Learning same as computer vision?

Computer vision is a subfield of AI that seeks to make computers understand the contents of the digital data contained within images or videos and make some sense out of them. Deep learning aims to bring machine learning one step closer to one of its original goals, that is, artificial intelligence.

What are the common classes of problems in machine learning?

Generally there are two main types of machine learning problems: supervised and unsupervised. Supervised machine learning problems are problems where we want to make predictions based on a set of examples.

Is deep learning the future of computer vision?

The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific problems.

Is deep learning the end of digital image processing?

Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete.

Can hybrid methodologies improve computer vision performance?

Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning.

What is the best dataset for multiple computer vision tasks?

Another dataset for multiple computer vision tasks is Microsoft’s Common Objects in Context Dataset, often referred to as MS COCO. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, 2014.