The 10th Israel Machine Vision Conference (IMVC) took place on March 18, 2019 at Pavilion 10, EXPO Tel Aviv.
Dr. Leonid Karlinsky spoke at a conference on “Few-Shot Object X, or How Can We Train A DL Model with Only Few Examples”.
Learning to classify and localize instances of objects that belong to new categories, while training on just one or very few examples, is a long-standing challenge in modern computer vision. This problem is generally referred to as ‘few-shot learning’. It is particularly challenging for modern deep-learning based methods, which tend to be notoriously hungry for training data. In this talk I will cover several of our recent research papers offering advances on these problems using example synthesis (hallucination) and metric learning techniques and achieving state-of-the-art results on known and new few-shot benchmarks. In addition to covering the relatively well studied few-shot classification task, I will show how our approaches can address the yet under-studied few-shot localization and multi-label few-shot classification tasks. In addition to this talk, a detailed tutorial covering the few-shot learning field in general will be given by Dr. Joseph Shtok from my team.
Dr. Karlinsky leads the CV & DL research team in the Computer Vision and Augmented Reality (CVAR) group @ IBM Research AI. He is a Computer Vision and Machine Learning expert with years of hands on experience. He is actively publishing research papers in leading CV and ML venues such as ECCV, CVPR and NIPS and is actively reviewing for these conferences for the past 10 years. Dr. Karlinsky holds a PhD degree in CV from the Weizmann Institute of Science, supervised by Prof. Shimon Ullman.
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