1 code implementation • 3 Dec 2023 • Piotr Teterwak, Soren Nelson, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer
To address this, we generate layer weights by learning to compose sets of SuperWeights, which represent a group of trainable parameters.
1 code implementation • 30 Nov 2023 • Dina Bashkirova, Arijit Ray, Rupayan Mallick, Sarah Adel Bargal, Jianming Zhang, Ranjay Krishna, Kate Saenko
Although generative editing methods now enable some forms of image editing, relighting is still beyond today's capabilities; existing methods struggle to keep other aspects of the image -- colors, shapes, and textures -- consistent after the edit.
1 code implementation • 26 Mar 2023 • Dina Bashkirova, Samarth Mishra, Diala Lteif, Piotr Teterwak, Donghyun Kim, Fadi Alladkani, James Akl, Berk Calli, Sarah Adel Bargal, Kate Saenko, Daehan Kim, Minseok Seo, YoungJin Jeon, Dong-Geol Choi, Shahaf Ettedgui, Raja Giryes, Shady Abu-Hussein, Binhui Xie, Shuang Li
To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
2 code implementations • CVPR 2023 • Dina Bashkirova, Jose Lezama, Kihyuk Sohn, Kate Saenko, Irfan Essa
We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation.
no code implementations • 26 Nov 2021 • Ben Usman, Dina Bashkirova, Kate Saenko
Unsupervised image-to-image translation methods aim to map images from one domain into plausible examples from another domain while preserving structures shared across two domains.
no code implementations • 29 Sep 2021 • Piotr Teterwak, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer
We improve on these methods with MixtureEnsembles, which learns to factorize ensemble members with shared parameters by constructing each layer with a linear combination of templates.
1 code implementation • 23 Jul 2021 • Dina Bashkirova, Dan Hendrycks, Donghyun Kim, Samarth Mishra, Kate Saenko, Kuniaki Saito, Piotr Teterwak, Ben Usman
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i. e., the same domain.
no code implementations • 23 Jul 2021 • Andrey Zhmoginov, Dina Bashkirova, Mark Sandler
From practical perspective, our approach allows to: (a) reuse existing modules for learning new task by adjusting the computation order, (b) use it for unsupervised multi-source domain adaptation to illustrate that adaptation to unseen data can be achieved by only manipulating the order of pretrained modules, (c) show how our approach can be used to increase accuracy of existing architectures for image classification tasks such as ImageNet, without any parameter increase, by reusing the same block multiple times.
1 code implementation • CVPR 2022 • Dina Bashkirova, Mohamed Abdelfattah, Ziliang Zhu, James Akl, Fadi Alladkani, Ping Hu, Vitaly Ablavsky, Berk Calli, Sarah Adel Bargal, Kate Saenko
Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets.
1 code implementation • 29 Mar 2021 • Dina Bashkirova, Ben Usman, Kate Saenko
Given an input image from a source domain and a guidance image from a target domain, unsupervised many-to-many image-to-image (UMMI2I) translation methods seek to generate a plausible example from the target domain that preserves domain-invariant information of the input source image and inherits the domain-specific information from the guidance image.
1 code implementation • NeurIPS 2019 • Dina Bashkirova, Ben Usman, Kate Saenko
The goal of unsupervised image-to-image translation is to map images from one domain to another without the ground truth correspondence between the two domains.
1 code implementation • ICLR 2019 • Dina Bashkirova, Ben Usman, Kate Saenko
Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time.