Zobrazeno 1 - 10
of 3 111
pro vyhledávání: '"PATEL, VISHAL"'
Face parsing refers to the semantic segmentation of human faces into key facial regions such as eyes, nose, hair, etc. It serves as a prerequisite for various advanced applications, including face editing, face swapping, and facial makeup, which ofte
Externí odkaz:
http://arxiv.org/abs/2412.08647
Autor:
Narayan, Kartik, Nair, Nithin Gopalakrishnan, Xu, Jennifer, Chellappa, Rama, Patel, Vishal M.
Pre-training on large-scale datasets and utilizing margin-based loss functions have been highly successful in training models for high-resolution face recognition. However, these models struggle with low-resolution face datasets, in which the faces l
Externí odkaz:
http://arxiv.org/abs/2412.07771
All-weather image restoration (AWIR) is crucial for reliable autonomous navigation under adverse weather conditions. AWIR models are trained to address a specific set of weather conditions such as fog, rain, and snow. But this causes them to often st
Externí odkaz:
http://arxiv.org/abs/2411.17814
Deep learning-based models for All-In-One Image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor generalization to samples outside the training distribution. This lim
Externí odkaz:
http://arxiv.org/abs/2411.17687
Federated Learning (FL) is a form of distributed learning that allows multiple institutions or clients to collaboratively learn a global model to solve a task. This allows the model to utilize the information from every institute while preserving dat
Externí odkaz:
http://arxiv.org/abs/2410.24181
Autor:
Safaei, Bardia, Patel, Vishal M.
Pre-trained vision-language models (VLMs) like CLIP have demonstrated impressive zero-shot performance on a wide range of downstream computer vision tasks. However, there still exists a considerable performance gap between these models and a supervis
Externí odkaz:
http://arxiv.org/abs/2410.22187
Autor:
Korkmaz, Yilmaz, Patel, Vishal M.
Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modalities as it provides superior resolution of soft tissues, albeit with a notable limitation in scanning speed. The advent of deep learning has catalyzed the development
Externí odkaz:
http://arxiv.org/abs/2409.12401
Autor:
Korkmaz, Yilmaz, Patel, Vishal M.
Unpaired image-to-image translation is a challenging task due to the absence of paired examples, which complicates learning the complex mappings between the distinct distributions of the source and target domains. One of the most commonly used approa
Externí odkaz:
http://arxiv.org/abs/2409.12399
All-Weather Image Restoration (AWIR) under adverse weather conditions is a challenging task due to the presence of different types of degradations. Prior research in this domain relies on extensive training data but lacks the utilization of additiona
Externí odkaz:
http://arxiv.org/abs/2409.00263
Medical image segmentation has been traditionally approached by training or fine-tuning the entire model to cater to any new modality or dataset. However, this approach often requires tuning a large number of parameters during training. With the intr
Externí odkaz:
http://arxiv.org/abs/2408.06447