77 citations · 151 across the 13 of their papers we have counts for
6 papers · 1 filter
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Oran Lang, Yossi Gandelsman, Michal Yarom +8
Image classification models can depend on multiple different semantic attributes of the image. An explanation of the decision of the classifier needs to both discover and visualize…
Learning Object Detection from Captions via Textual Scene Attributes
Achiya Jerbi, Roei Herzig, Jonathan Berant +2
Object detection is a fundamental task in computer vision, requiring large annotated datasets that are difficult to collect, as annotators need to label objects and their bounding…
Learning Object Permanence from Video
Aviv Shamsian, Ofri Kleinfeld, Amir Globerson +1
Object Permanence allows people to reason about the location of non-visible objects, by understanding that they continue to exist even when not perceived directly. Object Permanenc…
Learning Canonical Representations for Scene Graph to Image Generation
Roei Herzig, Amir Bar, Huijuan Xu +3
Generating realistic images of complex visual scenes becomes challenging when one wishes to control the structure of the generated images. Previous approaches showed that scenes wi…
Differentiable Scene Graphs
Moshiko Raboh, Roei Herzig, Gal Chechik +2
Reasoning about complex visual scenes involves perception of entities and their relations. Scene graphs provide a natural representation for reasoning tasks, by assigning labels to…
Spatio-Temporal Action Graph Networks
Roei Herzig, Elad Levi, Huijuan Xu +5
Events defined by the interaction of objects in a scene are often of critical importance; yet important events may have insufficient labeled examples to train a conventional deep m…