Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Bahat, Yuval"'
Image restoration problems are typically ill-posed in the sense that each degraded image can be restored in infinitely many valid ways. To accommodate this, many works generate a diverse set of outputs by attempting to randomly sample from the poster
Externí odkaz:
http://arxiv.org/abs/2310.16047
Permutation matrices play a key role in matching and assignment problems across the fields, especially in computer vision and robotics. However, memory for explicitly representing permutation matrices grows quadratically with the size of the problem,
Externí odkaz:
http://arxiv.org/abs/2308.13252
Neural volumetric representations have become a widely adopted model for radiance fields in 3D scenes. These representations are fully implicit or hybrid function approximators of the instantaneous volumetric radiance in a scene, which are typically
Externí odkaz:
http://arxiv.org/abs/2212.04666
Probabilistic diffusion models have achieved state-of-the-art results for image synthesis, inpainting, and text-to-image tasks. However, they are still in the early stages of generating complex 3D shapes. This work proposes Diffusion-SDF, a generativ
Externí odkaz:
http://arxiv.org/abs/2211.13757
We introduce Neural Point Light Fields that represent scenes implicitly with a light field living on a sparse point cloud. Combining differentiable volume rendering with learned implicit density representations has made it possible to synthesize phot
Externí odkaz:
http://arxiv.org/abs/2112.01473
Autor:
Bahat, Yuval, Shakhnarovich, Gregory
Machine learning plays an increasingly significant role in many aspects of our lives (including medicine, transportation, security, justice and other domains), making the potential consequences of false predictions increasingly devastating. These con
Externí odkaz:
http://arxiv.org/abs/2006.16705
Autor:
Bahat, Yuval, Michaeli, Tomer
The ever-growing amounts of visual contents captured on a daily basis necessitate the use of lossy compression methods in order to save storage space and transmission bandwidth. While extensive research efforts are devoted to improving compression te
Externí odkaz:
http://arxiv.org/abs/2006.09332
Autor:
Bahat, Yuval, Michaeli, Tomer
Publikováno v:
Proceedings .of .the.IEEE/CVF.Conference.on.Computer.Vision.and.Pattern.Recognition. (2020) 2716-2725
Single image super resolution (SR) has seen major performance leaps in recent years. However, existing methods do not allow exploring the infinitely many plausible reconstructions that might have given rise to the observed low-resolution (LR) image.
Externí odkaz:
http://arxiv.org/abs/1912.01839
We propose an approach to distinguish between correct and incorrect image classifications. Our approach can detect misclassifications which either occur $\it{unintentionally}$ ("natural errors"), or due to $\it{intentional~adversarial~attacks}$ ("adv
Externí odkaz:
http://arxiv.org/abs/1902.00236
Autor:
Bahat, Yuval, Shakhnarovich, Gregory
We develop a technique for automatically detecting the classification errors of a pre-trained visual classifier. Our method is agnostic to the form of the classifier, requiring access only to classifier responses to a set of inputs. We train a parame
Externí odkaz:
http://arxiv.org/abs/1804.00657