Search Results for author:
Found 12 papers, 10 papers with code
Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains
This algorithm works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring.
HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input.
Single-Image HDR Reconstruction by Multi-Exposure Generation
In this work, we propose a weakly supervised learning method that inverts the physical image formation process for HDR reconstruction via learning to generate multiple exposures from a single image.
PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement
The extremes of lighting (e. g. too much or too little light) usually cause many troubles for machine and human vision.
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
To the best of our knowledge, our work is the first to explore transductive few-shot video classification.
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
In this work, we propose to use out-of-distribution samples, i. e., unlabeled samples coming from outside the target classes, to improve few-shot learning.
TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation
Our study outlines several issues in the current evaluation pipeline: (i) for image quality assessment, a commonly used metric, e. g., Inception Score (IS), is often either miscalibrated for the single-object case or misused for the multi-object case; (ii) for text relevance and object accuracy assessment, there is an overfitting phenomenon in the existing R-precision (RP) and Semantic Object Accuracy (SOA) metrics, respectively; (iii) for multi-object case, many vital factors for evaluation, e. g., object fidelity, positional alignment, counting alignment, are largely dismissed; (iv) the ranking of the methods based on current metrics is highly inconsistent with real images.
HyperInverter: Improving StyleGAN Inversion via Hypernetwork
In the first phase, we train an encoder to map the input image to StyleGAN2 $\mathcal{W}$-space, which was proven to have excellent editability but lower reconstruction quality.
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples NeurIPS 2021
In this work, we propose to use out-of-distribution samples, i. e., unlabeled samples coming from outside the target classes, to improve few-shot learning.
Group-Theme Recoloring for Multi-Image Color Consistency
Methods such as color transfer are effective in making an image share similar colors with a target image; however, color transfer is not suitable for modifying multiple images.
Illuminant Aware Gamut-Based Color Transfer
Our method is unique in its considera- tion of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image.
Raw-to-Raw: Mapping between Image Sensor Color Responses
We show that this approach achieves state-of-the-art results on a range of consumer cameras and images of arbitrary scenes and illuminations.
