8th January 2018, Diane Larlus: “Visual Search in Large Image Collections”

Querying with an example image is a simple and intuitive interface to retrieve information from a collection of images. Such a retrieval task has a wide range of applications, including reverse image search on the web or the automatic organization of personal photo collections. While deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it was falling short when applied to these retrieval tasks. This presentation will show how to successfully apply deep learning representations such as convolutional neural networks to visual search, producing a solution that is both effective and computationally efficient.​

In a second part, the presentation will move beyond instance-level retrieval and consider the task of semantic image retrieval in complex scenes, where the goal is to retrieve images that share the same semantics as the query image. Despite being more subjective and more complex, one can show that the task of semantically ranking visual scenes is consistently implemented across a pool of human annotators, and that deep learning models can also be leveraged to automate this task of semantic retrieval.​

Station F is the world’s largest startup campus where NAVER LINE is one of the biggest partners with 80 seats.