אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

While Deep learning has brought a huge advancement to computer vision, for most tasks we still need hundreds of labeled samples per class. The few-shot learning tasks attempts to alleviate the data problem by learning from 1/ 5 samples per class. We will discuss the few-shot learning domain through two of my papers. The first paper LaSO, is a SOTA augmentation mechanic for multi-label few-shot classification and was published in CVPR 2019. The second paper StarNet is the SOTA weakly-supervised few-shot object localization and detection method and was presented in AAAI 2021. Short bio Amit is a direct Ph.D. candidate at the Electrical engineering faculty under the supervision of prof. Alex Bronstein from the CS faculty. Amit also works part-time at IBM research AI.