Pre-trained image processing transformer
As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …
Learning a sparse transformer network for effective image deraining
Transformers-based methods have achieved significant performance in image deraining as
they can model the non-local information which is vital for high-quality image reconstruction …
they can model the non-local information which is vital for high-quality image reconstruction …
Semantic image segmentation: Two decades of research
Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …
vision applications, providing key information for the global understanding of an image. This …
Unsupervised domain adaptation for semantic image segmentation: a comprehensive survey
Semantic segmentation plays a fundamental role in a broad variety of computer vision
applications, providing key information for the global understanding of an image. Yet, the …
applications, providing key information for the global understanding of an image. Yet, the …
Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening
Enhancing the generalization capability of deep neural networks to unseen domains is
crucial for safety-critical applications in the real world such as autonomous driving. To …
crucial for safety-critical applications in the real world such as autonomous driving. To …
Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for image dehazing
In recent years, Transformer networks are beginning to replace pure convolutional neural
networks (CNNs) in the field of computer vision due to their global receptive field and …
networks (CNNs) in the field of computer vision due to their global receptive field and …
3d common corruptions and data augmentation
We introduce a set of image transformations that can be used as corruptions to evaluate the
robustness of models as well as data augmentation mechanisms for training neural …
robustness of models as well as data augmentation mechanisms for training neural …
Single image deraining: From model-based to data-driven and beyond
The goal of single-image deraining is to restore the rain-free background scenes of an
image degraded by rain streaks and rain accumulation. The early single-image deraining …
image degraded by rain streaks and rain accumulation. The early single-image deraining …
Learning weather-general and weather-specific features for image restoration under multiple adverse weather conditions
Image restoration under multiple adverse weather conditions aims to remove weather-
related artifacts by using the single set of network parameters. In this paper, we find that …
related artifacts by using the single set of network parameters. In this paper, we find that …
Syn2real transfer learning for image deraining using gaussian processes
Recent CNN-based methods for image deraining have achieved excellent performance in
terms of reconstruction error as well as visual quality. However, these methods are limited in …
terms of reconstruction error as well as visual quality. However, these methods are limited in …