no code implementations • 4 Apr 2024 • Michelle Shu, Charles Herrmann, Richard Strong Bowen, Forrester Cole, Ramin Zabih
Text-conditioned diffusion models can generate impressive images, but fall short when it comes to fine-grained control.
no code implementations • 29 Mar 2024 • Ali Behrouz, Michele Santacatterina, Ramin Zabih
Motivated by the success of SSMs, we present MambaMixer, a new architecture with data-dependent weights that uses a dual selection mechanism across tokens and channels, called Selective Token and Channel Mixer.
no code implementations • 1 Oct 2023 • Khiem Pham, David A. Hirshberg, Phuong-Mai Huynh-Pham, Michele Santacatterina, Ser-Nam Lim, Ramin Zabih
Our experiments on synthetic and semi-synthetic data demonstrate that our method has competitive bias and smaller variance than debiased machine learning approaches.
2 code implementations • 22 Feb 2023 • Yifei Zhou, Juntao Ren, Fengyu Li, Ramin Zabih, Ser-Nam Lim
Advances in the field of vision-language contrastive learning have made it possible for many downstream applications to be carried out efficiently and accurately by simply taking the dot product between image and text representations.
1 code implementation • 20 Oct 2022 • John X. Morris, Justin T. Chiu, Ramin Zabih, Alexander M. Rush
We propose an unsupervised deidentification method that masks words that leak personally-identifying information.
no code implementations • 2 Dec 2021 • Richard Strong Bowen, Richard Tucker, Ramin Zabih, Noah Snavely
We introduce a way to learn to estimate a scene representation from a single image by predicting a low-dimensional subspace of optical flow for each training example, which encompasses the variety of possible camera and object movement.
1 code implementation • CVPR 2022 • Charles Herrmann, Kyle Sargent, Lu Jiang, Ramin Zabih, Huiwen Chang, Ce Liu, Dilip Krishnan, Deqing Sun
In this work, we present pyramid adversarial training (PyramidAT), a simple and effective technique to improve ViT's overall performance.
Ranked #9 on Domain Generalization on ImageNet-C (using extra training data)
no code implementations • ICCV 2021 • Michelle Shu, Richard Strong Bowen, Charles Herrmann, Gengmo Qi, Michele Santacatterina, Ramin Zabih
Time-to-event analysis is an important statistical tool for allocating clinical resources such as ICU beds.
no code implementations • CVPR 2021 • Richard Strong Bowen, Huiwen Chang, Charles Herrmann, Piotr Teterwak, Ce Liu, Ramin Zabih
Existing methods struggle to extrapolate images with salient objects in the foreground or are limited to very specific objects such as humans, but tend to work well on indoor/outdoor scenes.
1 code implementation • CVPR 2021 • Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, Ce Liu
Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications.
no code implementations • ECCV 2018 • Charles Herrmann, Chen Wang, Richard Strong Bowen, Emil Keyder, Michael Krainin, Ce Liu, Ramin Zabih
Here, we observe that the use of a single registration often leads to errors, especially in scenes with significant depth variation or object motion.
no code implementations • ECCV 2018 • Charles Herrmann, Chen Wang, Richard Strong Bowen, Emil Keyder, Ramin Zabih
Image stitching is typically decomposed into three phases: registration, which aligns the source images with a common target image; seam finding, which determines for each target pixel the source image it should come from; and blending, which smooths transitions over the seams.
no code implementations • CVPR 2020 • Charles Herrmann, Richard Strong Bowen, Neal Wadhwa, Rahul Garg, Qiurui He, Jonathan T. Barron, Ramin Zabih
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance.
1 code implementation • ECCV 2020 • Charles Herrmann, Richard Strong Bowen, Ramin Zabih
Important applications such as mobile computing require reducing the computational costs of neural network inference.
no code implementations • ICCV 2017 • Chen Wang, Charles Herrmann, Ramin Zabih
While Markov Random Fields (MRFs) are widely used in computer vision, they present a quite challenging inference problem.
no code implementations • CVPR 2016 • Chen Wang, Ramin Zabih
Markov Random Fields (MRFs) are a widely used graphical model, but the inference problem is NP-hard.
no code implementations • 17 Apr 2016 • Ramin Zabih
Example-based super-resolution (EBSR) reconstructs a high-resolution image from a low-resolution image, given a training set of high-resolution images.
no code implementations • CVPR 2014 • Alexander Fix, Chen Wang, Ramin Zabih
Graph cuts method such as a-expansion [4] and fusion moves [22] have been successful at solving many optimization problems in computer vision.
no code implementations • 28 Sep 2013 • Alexander Fix, Thorsten Joachims, Sam Park, Ramin Zabih
Rather than trying to formulate existing higher order priors as an SoS function, we take a discriminative learning approach, effectively searching the space of SoS functions for a higher order prior that performs well on our training set.