Search Results for author:
Found 14 papers, 5 papers with code
Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning
Arbitrary style transfer holds widespread attention in research and boasts numerous practical applications.
Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning
By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes.
Rethinking Fast Fourier Convolution in Image Inpainting
Recently proposed image inpainting method LaMa builds its network upon Fast Fourier Convolution (FFC), which was originally proposed for high-level vision tasks like image classification.
MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer
The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations.
AesUST: Towards Aesthetic-Enhanced Universal Style Transfer
Specifically, our approach introduces an aesthetic discriminator to learn the universal human-delightful aesthetic features from a large corpus of artist-created paintings.
Texture Reformer: Towards Fast and Universal Interactive Texture Transfer
In addition, we also introduce a novel learning-free view-specific texture reformation (VSTR) operation with a new semantic map guidance strategy to achieve more accurate semantic-guided and structure-preserved texture transfer.
Artistic Style Transfer with Internal-external Learning and Contrastive Learning
Although existing artistic style transfer methods have achieved significant improvement with deep neural networks, they still suffer from artifacts such as disharmonious colors and repetitive patterns.
DualAST: Dual Style-Learning Networks for Artistic Style Transfer
Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles.
DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer
Gram-based and patch-based approaches are two important research lines of style transfer.
Diverse Image Style Transfer via Invertible Cross-Space Mapping
Image style transfer aims to transfer the styles of artworks onto arbitrary photographs to create novel artistic images.
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization
Multimodal image-to-image translation (I2IT) aims to learn a conditional distribution that explores multiple possible images in the target domain given an input image in the source domain.
LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching
Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images.
Cascade Style Transfer
This could help improve the quality and flexibility, and guide us to find domain-independent approaches.
On Compression of Unsupervised Neural Nets by Pruning Weak Connections
Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep Belif Networks(DBNs), are powerful in automatic feature extraction, unsupervised weight initialization and density estimation.
