Search Results for author:
Found 7 papers, 7 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.
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.
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).
