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Found 14 papers, 14 papers with code
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models
Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets.
Controlling Vision-Language Models for Multi-Task Image Restoration
In this paper, we present a degradation-aware vision-language model (DA-CLIP) to better transfer pretrained vision-language models to low-level vision tasks as a multi-task framework for image restoration.
Ranked #1 on
Single Image Deraining
on Rain100H
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
This work aims to improve the applicability of diffusion models in realistic image restoration.
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?
We then employ our benchmark to evaluate many of the most common uncertainty estimation methods, as well as two state-of-the-art uncertainty scores from the task of out-of-distribution detection.
Image Restoration with Mean-Reverting Stochastic Differential Equations
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration.
Ranked #2 on
Single Image Deraining
on Rain100H
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods
We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations.
Learning Proposals for Practical Energy-Based Regression
Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression.
Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI
The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120, 000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.
Accurate 3D Object Detection using Energy-Based Models
On the KITTI dataset, our proposed approach consistently outperforms the SA-SSD baseline across all 3DOD metrics, demonstrating the potential of EBM-based regression for highly accurate 3DOD.
Ranked #1 on
3D Object Detection
on KITTI Cars Easy val
Deep Energy-Based NARX Models
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data.
How to Train Your Energy-Based Model for Regression
While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking.
Ranked #1 on
Visual Object Tracking
on OTB-100
Energy-Based Models for Deep Probabilistic Regression
In our proposed approach, we create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x, y).
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning.
