Dear All All are invited for the same What : Talk on Object Detection in Presence of Hard Examples When : 11 March 2016 , 5:00 PM - 6:00 PM Where : KD 101 (HRKD Building) Who : Subhabrata Debnath *Abstract* Object detection involves finding the location of the object of interest in an image. This is done by learning a detector using training images containing the location of the object as input. Thus the robustness of the detector directly depends on the quality of training set. In our work we try to achieve robust detection even in the presence of visually hard images in the training data. In our setting, we are presented with labels which indicate the presence or absence of an object in an image but not their explicit locations. This is called a weakly supervised setting. We aim to learn a detector which can classify a test image as well as find the location of the bounding box containing the object in the image. This can be done by modeling the location of the object as a latent parameter and learning both the location and the classifier jointly during training. We show how using a variation of Outliers Robust-SVM and Self paced learning with latent variables can be used to obtain good results in this scenario. We show our results on three classes of the Pascal Voc 2007 dataset and present a comparison with existing methods. *Bio :* Subhabrata Debnath is Co-Founder and Computer Vision Researcher @ VisageMap Inc. He previously worked in Information Systems Officer at Indian Oil Corporation Limited(IOCL) . He did his Mtech in CSE 2013 -2015 from IIT Kanpur and Btech from Bengal Institute of Technology, Kolkata in 2008 - 2012. Publication : Adapting Ransac SVM to detect outliers for robust classification, BMVC 2015, [Subhabrata Debnath, Anjan Banerjee, Vinay Namboodiri](http://www.bmva.org/bmvc/2015/papers/paper168/index.html) Yours Sincerely Vivek Gupta