Name: Explainable, Adaptive, and Cross-Domain Few-Shot Learning - Dr. Leonid Karlinsky
Start: 2021-01-10T17:00:00+02:00
End: 2021-01-10T18:30:00+02:00

In this talk we will discuss our recent advances in few-shot learning, a regime where only a handful of training examples (maybe just one) are available for learning novel categories unseen during training. We will cover a method for few-shot classification that is capable of matching and localizing instances of novel categories, despite being trained and used with only category level image labels and without any location supervision, also opening the door for weakly supervised few-shot detection. We will cover a method for meta-learning a model that automatically modifies its architecture to better adapt to novel few-shot tasks. Finally, we will discuss the limitation of the current few-shot learning methods when handling extreme cases of domain transfer, and offer a new benchmark and some ideas towards cross-domain few-shot learning.

The talk is based on the papers:

A Broader Study of Cross-Domain Few-Shot Learning (ECCV 2020)
arxiv: https://arxiv.org/abs/1912.07200
git: https://github.com/IBM/cdfsl-benchmark

StarNet: towards Weakly Supervised Few-Shot Object Detection (AAAI 2021)
arxiv: https://arxiv.org/abs/2003.06798

TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification (ECCV 2020)
arxiv: https://arxiv.org/abs/2003.06670

Presenter BIO:

Leonid Karlinsky leads the CV & DL research team in the Computer Vision and Augmented Reality (CVAR) group @ IBM Research AI. Before joining IBM, he served as a research scientist in Applied Materials, Elbit, and FDNA. He is actively publishing and reviewing at ECCV, ICCV, CVPR and NeurIPS, and is serving as an IMVC steering committee member for the past 3 years. His recent research is in the areas of few-shot learning with specific focus on object detection, metric learning, and example synthesis methods. He received his PhD degree at the Weizmann Institute of Science, supervised by Prof. Shimon Ullman.

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