Personal Home Page: http://researchweb.iiit.ac.in/~yashaswi.verma/
Yashaswi Verma, C.V. Jawahar - Exploring Locally Rigid Discriminative Patched for Learning Relative Attributes Proceedings of the 26th British Machine Vision Conference, 07-10 Sep 2015, Swansea, UK. [PDF]
Ramachandruni N. Sandeep, Yashaswi Verma and C.V. Jawahar - Relative Parts : Distinctive Parts of Learning Relative Attributes Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 23-28 June 2014, Columbus, Ohio, USA. [PDF]
Sandeep, Ramachandruni N, Yashaswi Verma and C.V. Jawahar - Relative parts: Distinctive parts for learning relative attributes Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014. [PDF]
Yashaswi Verma, Ankush Gupta, Prashanth Mannem and C.V. Jawahar - Generating image descriptions using semantic similarities in the output space Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2013. [PDF]
Yashaswi Varma and C V Jawahar - Image Annotation using Metric Learning in Semantic Neighbourhoods Proceedings of 12th European Conference on Computer Vision, 7-13 Oct. 2012, Print ISBN 978-3-642-33711--6, Vol. ECCV 2012, Part-III, LNCS 7574, pp. 114-128, Firenze, Italy. [PDF]
Our aim is to learn relative attributes using local parts that are shared across categories. First, instead of using a global representation, we introduce a part-based representation combining a pair of images that specifically compares corresponding parts. Then, with each part we associate a locally adaptive “significance coefficient” that represents its discriminative ability with respect to a particular attribute. For each attribute, the significance-coefficients are learned simultaneously with a max-margin ranking model in an iterative manner. Compared to the baseline method , the new method is shown to achieve significant improvements in relative attribute prediction accuracy. Additionally, it is also shown to improve relative feedback based interactive image search.
In many real-life scenarios, an object can be categorized into multiple categories. E.g., a newspaper column can be tagged as "political", "election", "democracy"; an image may contain "tiger", "grass", "river"; and so on. These are instances of multi-label classification, which deals with the task of associating multiple labels with single data. Automatic image annotation is a multi-label classification problem that aims at associating a set of text with an image that describes its semantics.