Search Results for author:
Found 19 papers, 9 papers with code
Optimizing for the Shortest Path in Denoising Diffusion Model
In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error.
Integral Fast Fourier Color Constancy
Traditional auto white balance (AWB) algorithms typically assume a single global illuminant source, which leads to color distortions in multi-illuminant scenes.
RGB-Event ISP: The Dataset and Benchmark
In summary, to the best of our knowledge, this is the very first research focusing on event-guided ISP, and we hope it will inspire the community.
Learning Triangular Distribution in Visual World
Convolution neural network is successful in pervasive vision tasks, including label distribution learning, which usually takes the form of learning an injection from the non-linear visual features to the well-defined labels.
Point Cloud Color Constancy
In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud.
Illumination Estimation Challenge: experience of past two years
The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased.
Fast Fourier Intrinsic Network
We address the problem of decomposing an image into albedo and shading.
SDE-AWB: a Generic Solution for 2nd International Illumination Estimation Challenge
We propose a neural network-based solution for three different tracks of 2nd International Illumination Estimation Challenge (chromaticity. iitp. ru).
A Benchmark for Temporal Color Constancy
The conventional approach is to use a single frame - shot frame - to estimate the scene illumination color.
Cascading Convolutional Color Constancy
Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy.
DAL -- A Deep Depth-aware Long-term Tracker
In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run.
Fast Fourier Color Constancy and Grayness Index for ISPA Illumination Estimation Challenge
We briefly introduce two submissions to the Illumination Estimation Challenge, in the Int'l Workshop on Color Vision, affiliated to the 11th Int'l Symposium on Image and Signal Processing and Analysis.
Flash Lightens Gray Pixels
In the real world, a scene is usually cast by multiple illuminants and herein we address the problem of spatial illumination estimation.
On Finding Gray Pixels
We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation.
Object Detection in Equirectangular Panorama
We introduced a high-resolution equirectangular panorama (360-degree, virtual reality) dataset for object detection and propose a multi-projection variant of YOLO detector.
Revisiting Gray Pixel for Statistical Illumination Estimation
We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering.
Recurrent Color Constancy
We introduce a novel formulation of temporal color constancy which considers multiple frames preceding the frame for which illumination is estimated.
Convolutional Low-Resolution Fine-Grained Classification
Successful fine-grained image classification methods learn subtle details between visually similar (sub-)classes, but the problem becomes significantly more challenging if the details are missing due to low resolution.
Deep Structured-Output Regression Learning for Computational Color Constancy
Computational color constancy that requires esti- mation of illuminant colors of images is a fundamental yet active problem in computer vision, which can be formulated into a regression problem.
