1 citations · 1 across the 1 of their papers we have counts for
7 papers
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization
Tzuf Paz-Argaman, Yuval Atzmon, Gal Chechik +1
We study the problem of recognizing visual entities from the textual descriptions of their classes. Specifically, given birds' images with free-text descriptions of their species,…
A causal view of compositional zero-shot recognition
Yuval Atzmon, Felix Kreuk, Uri Shalit +1
People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domai…
From Generalized zero-shot learning to long-tail with class descriptors
Dvir Samuel, Yuval Atzmon, Gal Chechik
Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. Often, classes can be accompani…
Cooperative image captioning
Gilad Vered, Gal Oren, Yuval Atzmon +1
When describing images with natural language, the descriptions can be made more informative if tuned using downstream tasks. This is often achieved by training two networks: a "spe…
Few-Shot Learning with Per-Sample Rich Supervision
Roman Visotsky, Yuval Atzmon, Gal Chechik
Learning with few samples is a major challenge for parameter-rich models like deep networks. In contrast, people learn complex new concepts even from very few examples, suggesting…
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning
Yuval Atzmon, Gal Chechik
Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes…