Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects
Estimating the pose of an uncooperative spacecraft is an important computer vision problem
for enabling the deployment of automatic vision-based systems in orbit, with applications …
for enabling the deployment of automatic vision-based systems in orbit, with applications …
ClimaX: A foundation model for weather and climate
Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …
informed numerical models of the atmosphere. These approaches aim to model the non …
Are transformers more robust than cnns?
Transformer emerges as a powerful tool for visual recognition. In addition to demonstrating
competitive performance on a broad range of visual benchmarks, recent works also argue …
competitive performance on a broad range of visual benchmarks, recent works also argue …
Video transformers: A survey
Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …
them a promising tool for modeling video. However, they lack inductive biases and scale …
Part-aware transformer for generalizable person re-identification
Abstract Domain generalization person re-identification (DG ReID) aims to train a model on
source domains and generalize well on unseen domains. Vision Transformer usually yields …
source domains and generalize well on unseen domains. Vision Transformer usually yields …
A survey of the vision transformers and their CNN-transformer based variants
Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
networks (CNNs) for a variety of computer vision applications. These transformers, with their …
Delving into masked autoencoders for multi-label thorax disease classification
Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
An impartial take to the cnn vs transformer robustness contest
Following the surge of popularity of Transformers in Computer Vision, several studies have
attempted to determine whether they could be more robust to distribution shifts and provide …
attempted to determine whether they could be more robust to distribution shifts and provide …
A closer look at the robustness of contrastive language-image pre-training (clip)
Abstract Contrastive Language-Image Pre-training (CLIP) models have demonstrated
remarkable generalization capabilities across multiple challenging distribution shifts …
remarkable generalization capabilities across multiple challenging distribution shifts …