Efficient visual learning with reduced supervision

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We are currently witnessing an incredible demand for the detection and recognition of visual objects in real-world systems and applications, such as portable devices and moving vehicles. Visual recognition is at once the key and the limitation towards the next generation of intelligent applications, such as autonomous driving, robotics in unconstrained environments and augmented reality. Recognition models are all currently based on a learning paradigm in which training data (i.e. examples) are provided to machines to automatically perform a complex task. This data-based approach to machine learning is particularly timely given the increasingly vast amount of information being generated and shared over the internet.******Modern machine learning algorithms such as deep convolutional neural networks can leverage massive amounts of data as training examples. However, two main challenges must be addressed:***i) Current state-of-the-arts methods in visual recognition/learning are based on powerful brute-force approaches, which do not reason about when and how to use the available computation. The high computational cost of these techniques is then the limiting factor for learning with massive data and for deploying these recognition algorithms on computation-limited platforms, such as embedded and low power devices.***ii) Standard learning algorithms require not only training samples (input), but also the corresponding annotations or labels (output). Given that the available "Big Data" are equipped with little to no annotation, experts are required to perform an additional annotation step (supervision) to leverage it for learning. Thus, in these conditions, supervised annotation of training data often becomes one of the bottlenecks for learning on Big Data, in terms of both cost and feasibility.******The primary contribution of my research will be to develop computationally efficient methods for visual learning with reduced supervision. This research will focus on an in-depth study of the properties of data and algorithms that allow intelligent and efficient learning with reduced annotations. This will include: i) efficient techniques for visual learning and inference, ii) learning with reduced supervision, iii) visual data exploration.***This research is highly relevant and applicable to visual recognition tasks in various data modalities, including images (object classification, localization, segmentation), videos (action recognition and sentiment analysis), and text (document analysis and language modelling).******I expect that my future research can lead to effective and robust visual learning with reduced supervision. Long term, I expect that in this era of "Big Data", learning efficiently and with reduced supervision will be a key factor to further develop machine learning techniques in widespread applications.**