The recent successes in computer vision have been mostly around using a huge corpus of intricately labeled data for training recognition models. But, in real-world cases, acquiring such large datasets will require a lot of manual annotation, which may be strenuous, out of budget, or even prone to errors. Whereas, a lot of real data that are generated daily can be acquired at low to no annotation cost. Such data can be unlabeled or contain tag/meta-data information, termed as weak annotation. Our goal is to develop methods that can learn recognition models from such data involving limited manual supervision. In this thesis, we explore two dimensions of learning with limited supervision - first, reducing the number of manually labeled data re...
Statistical machine learning techniques have transformed computer vision research in the last two de...
We focus on two broad learning setups: The first one is the classic semi-supervised learning (SSL), ...
In the age of big data and machine learning the costs to turn the data into fuel for the algorithms ...
Deep neural networks have led to remarkable progress in visual recognition. A key driving factor is ...
In recent years, large-scale datasets with high-quality annotations have enabled many significant di...
In recent years, large-scale datasets with high-quality annotations have enabled many significant di...
Existing fully supervised deep learning methods usually require a large number of training samples w...
Machine learning models have led to remarkable progress in visual recognition. A key driving factor ...
Machine learning models have led to remarkable progress in visual recognition. A key driving factor ...
A fundamental key-point for the recent success of deep learning models is the availability of large ...
A fundamental key-point for the recent success of deep learning models is the availability of large ...
There has been historic progress in the field of image understanding over the past few years. Deep l...
Modern Computer Vision systems learn visual concepts through examples (i.e. images) which have been ...
Modern Computer Vision systems learn visual concepts through examples (i.e. images) which have been ...
There has been historic progress in the field of image understanding over the past few years. Deep l...
Statistical machine learning techniques have transformed computer vision research in the last two de...
We focus on two broad learning setups: The first one is the classic semi-supervised learning (SSL), ...
In the age of big data and machine learning the costs to turn the data into fuel for the algorithms ...
Deep neural networks have led to remarkable progress in visual recognition. A key driving factor is ...
In recent years, large-scale datasets with high-quality annotations have enabled many significant di...
In recent years, large-scale datasets with high-quality annotations have enabled many significant di...
Existing fully supervised deep learning methods usually require a large number of training samples w...
Machine learning models have led to remarkable progress in visual recognition. A key driving factor ...
Machine learning models have led to remarkable progress in visual recognition. A key driving factor ...
A fundamental key-point for the recent success of deep learning models is the availability of large ...
A fundamental key-point for the recent success of deep learning models is the availability of large ...
There has been historic progress in the field of image understanding over the past few years. Deep l...
Modern Computer Vision systems learn visual concepts through examples (i.e. images) which have been ...
Modern Computer Vision systems learn visual concepts through examples (i.e. images) which have been ...
There has been historic progress in the field of image understanding over the past few years. Deep l...
Statistical machine learning techniques have transformed computer vision research in the last two de...
We focus on two broad learning setups: The first one is the classic semi-supervised learning (SSL), ...
In the age of big data and machine learning the costs to turn the data into fuel for the algorithms ...