activity
20192022
most citedEnd to End Trainable Active Contours via Differentiable Rendering

7 citations · 21 across the 7 of their papers we have counts for

collaborators

11 papers

cs.LG20224 cited

Neural Inverse Kinematics

Raphael Bensadoun, Shir Gur, Nitsan Blau +2

Inverse kinematic (IK) methods recover the parameters of the joints, given the desired position of selected elements in the kinematic chain. While the problem is well-defined and l…

cs.CV2021

Meta Internal Learning

Raphael Bensadoun, Shir Gur, Tomer Galanti +1

Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a sing…

cs.CV2021

Cross-Modal Retrieval Augmentation for Multi-Modal Classification

Shir Gur, Natalia Neverova, Chris Stauffer +3

Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here,…

cs.CV20213 cited

Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers

Hila Chefer, Shir Gur, Lior Wolf

Transformers are increasingly dominating multi-modal reasoning tasks, such as visual question answering, achieving state-of-the-art results thanks to their ability to contextualize…

cs.CV20205 cited

Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization

Shir Gur, Ameen Ali, Lior Wolf

Neural network visualization techniques mark image locations by their relevancy to the network's classification. Existing methods are effective in highlighting the regions that aff…

cs.CV2020

Transformer Interpretability Beyond Attention Visualization

Hila Chefer, Shir Gur, Lior Wolf

Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks.…