Search Results for author:
Found 19 papers, 13 papers with code
CoTeRe-Net: Discovering Collaborative Ternary Relations in Videos
Current relation models mainly reason about relations of invisibly implicit cues, while important relations of visually explicit cues are rarely considered, and the collaboration between them is usually ignored.
Spiral Generative Network for Image Extrapolation
In this paper, motivated by human natural ability to perceive unseen surroundings imaginatively, we propose a novel Spiral Generative Network, SpiralNet, to perform image extrapolation in a spiral manner, which regards extrapolation as an evolution process growing from an input sub-image along a spiral curve to an expanded full image.
Face Forgery Detection with Elaborate Backbone
Specifically, we analyze the crucial contributions of backbones with different configurations in FFD task and propose leveraging the ViT network with self-supervised learning on real-face datasets to pre-train a backbone, equipping it with superior facial representation capabilities.
Advancing H&E-to-IHC Stain Translation in Breast Cancer: A Multi-Magnification and Attention-Based Approach
Our model employs a multi-magnification processing strategy to extract and utilize information from various magnifications within pathology images, facilitating robust image translation.
Video Harmonization with Triplet Spatio-Temporal Variation Patterns
Video harmonization is an important and challenging task that aims to obtain visually realistic composite videos by automatically adjusting the foreground's appearance to harmonize with the background.
Quantum Recurrent Neural Networks for Sequential Learning
Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources.
SinTra: Learning an inspiration model from a single multi-track music segment
SinTra, consisting of a pyramid of Transformer-XL with a multi-scale training strategy, can learn both the musical structure and the relative positional relationship between notes of the single training music segment.
SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception
In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions.
Intrinsic Image Harmonization
Specifically, we harmonize reflectance through material-consistency penalty, while harmonize illumination by learning and transferring light from background to foreground, moreover, we model patch relations between foreground and background of composite images in an inharmony-free learning way, to adaptively guide our intrinsic image harmonization.
Ranked #7 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
Image Harmonization With Transformer
Current solutions mainly adopt an encoder-decoder architecture with convolutional neural network (CNN) to capture the context of composite images, trying to understand what it looks like in the surrounding background near the foreground.
Ranked #11 on
Image Harmonization
on iHarmony4
Painting From Part
This paper studies the problem of painting the whole image from part of it, namely painting from part or part-painting for short, involving both inpainting and outpainting.
Multi-Modal Multi-Action Video Recognition
In this paper, we propose a novel multi-action relation model for videos, by leveraging both relational graph convolutional networks (GCNs) and video multi-modality.
ReshapeGAN: Object Reshaping by Providing A Single Reference Image
We present the tailored models of the proposed ReshapeGAN for all the problem settings, and have them tested on 8 kinds of reshaping tasks with 13 different datasets, demonstrating the ability of ReshapeGAN on generating convincing and superior results for object reshaping.
One-Shot Image-to-Image Translation via Part-Global Learning with a Multi-adversarial Framework
It is well known that humans can learn and recognize objects effectively from several limited image samples.
Generative Adversarial Network with Multi-Branch Discriminator for Cross-Species Image-to-Image Translation
Current approaches have made great progress on image-to-image translation tasks benefiting from the success of image synthesis methods especially generative adversarial networks (GANs).
Generative Adversarial Network
Image-to-Image Translation
+1
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network
Semantic layouts based Image synthesizing, which has benefited from the success of Generative Adversarial Network (GAN), has drawn much attention in these days.
Unpaired Photo-to-Caricature Translation on Faces in the Wild
We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks.
Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes
Generative Adversarial Networks are proved to be efficient on various kinds of image generation tasks.
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation
Image-to-image translation has been made much progress with embracing Generative Adversarial Networks (GANs).
