119 citations · 166 across the 13 of their papers we have counts for
13 papers
Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks
Guillermo Carbajal, Patricia Vitoria, José Lezama +1
In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp im…
MaskSketch: Unpaired Structure-guided Masked Image Generation
Dina Bashkirova, Jose Lezama, Kihyuk Sohn +2
Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels o…
Muse: Text-To-Image Generation via Masked Generative Transformers
Huiwen Chang, Han Zhang, Jarred Barber +9
We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregres…
Scaling Painting Style Transfer
Bruno Galerne, Lara Raad, José Lezama +1
Neural style transfer (NST) is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image. It is particularly impressive w…
MAGVIT: Masked Generative Video Transformer
Lijun Yu, Yong Cheng, Kihyuk Sohn +8
We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spat…
Visual Prompt Tuning for Generative Transfer Learning
Kihyuk Sohn, Yuan Hao, José Lezama +5
Transferring knowledge from an image synthesis model trained on a large dataset is a promising direction for learning generative image models from various domains efficiently. Whil…
Improved Masked Image Generation with Token-Critic
José Lezama, Huiwen Chang, Lu Jiang +1
Non-autoregressive generative transformers recently demonstrated impressive image generation performance, and orders of magnitude faster sampling than their autoregressive counterp…
Assessing the Role of Datasets in the Generalization of Motion Deblurring Methods to Real Images
Guillermo Carbajal, Patricia Vitoria, José Lezama +1
Successfully training end-to-end deep networks for real motion deblurring requires datasets of sharp/blurred image pairs that are realistic and diverse enough to achieve generaliza…
Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition
Guillermo Carbajal, Patricia Vitoria, Mauricio Delbracio +2
Motion blur estimation remains an important task for scene analysis and image restoration. In recent years, the removal of motion blur in photographs has seen impressive progress i…
Psychophysics, Gestalts and Games
José Lezama, Samy Blusseau, Jean-Michel Morel +2
Many psychophysical studies are dedicated to the evaluation of the human gestalt detection on dot or Gabor patterns, and to model its dependence on the pattern and background param…
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
José Lezama, Qiang Qiu, Pablo Musé +1
Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification. Yet, this does not naturally enforce intra-c…
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Qiang Qiu, Jose Lezama, Alex Bronstein +1
Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny…
Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral Hallucination and Low-rank Embedding
Jose Lezama, Qiang Qiu, Guillermo Sapiro
Surveillance cameras today often capture NIR (near infrared) images in low-light environments. However, most face datasets accessible for training and verification are only collect…