Adversarial Learning for Fine-Grained Image Search
While computer vision has been extensively studied, it still remains a challenging problem. In particular, fine-grained image search is a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories. Numerous algorithms using deep neural networks have achieved state-of-the-art
performance on fine-grained categorization, but they are not directly applicable to fine-grained image search. In this presentation, eBay’s Chief Scientist for Computer Vision, Robinson Piramuthu will present on eBay research that proposes a solution called FGGAN, which learns discriminative representations by implicitly studying geometric transformation from multi-view images for fine-grained image search.
As Chief Scientist for Computer Vision, Robinson drives eBay’s computer vision science strategy. With over 20 years of experience in computer vision, his expertise includes large scale visual search, coarse and fine-grained visual recognition, object detection, computer vision for fashion, 3D cues from 2D images, figure-ground segmentation and deep learning for vision, among other topics. Before joining eBay in 2011, he received his PhD in Electrical Engineering and Computer Science from the University of Michigan in 2000 specializing in information theory and statistical image processing. He also has a MS in control theory from the University of Florida, specializing in robust and nonlinear control systems.