Search Results for author:
Found 11 papers, 8 papers with code
Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption
The standard Neural Radiance Fields (NeRF) paradigm employs a viewer-centered methodology, entangling the aspects of illumination and material reflectance into emission solely from 3D points.
Aleth-NeRF: Low-light Condition View Synthesis with Concealing Fields
Common capture low-light scenes are challenging for most computer vision techniques, including Neural Radiance Fields (NeRF).
Improving Fairness in Image Classification via Sketching
Fairness is a fundamental requirement for trustworthy and human-centered Artificial Intelligence (AI) system.
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images.
You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction
Challenging illumination conditions (low-light, under-exposure and over-exposure) in the real world not only cast an unpleasant visual appearance but also taint the computer vision tasks.
Ranked #2 on
Image Enhancement
on Exposure-Errors
Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection
To enhance object detection in a dark environment, we propose a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation.
Ranked #1 on
2D Object Detection
on ExDark
Explainable Analysis of Deep Learning Methods for SAR Image Classification
Deep learning methods exhibit outstanding performance in synthetic aperture radar (SAR) image interpretation tasks.
MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection
In this paper, we introduce the first DETR framework for Monocular DEtection with a depth-guided TRansformer, named MonoDETR.
3D Object Detection From Monocular Images
Autonomous Driving
+3
Linear Array Network for Low-light Image Enhancement
However, the convolution operation is based on a local sliding window mechanism, which is difficult to construct the long-range dependencies of the feature maps.
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in degraded images.
Estimating and Improving Fairness with Adversarial Learning
To tackle this issue, we propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system.
