Search Results for author:
Found 11 papers, 5 papers with code
Multi-view Feature Augmentation with Adaptive Class Activation Mapping
We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance.
Visual Anomaly Detection for Images: A Survey
Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision.
Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection
For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet.
Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function
Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance.
Two-stage Training for Learning from Label Proportions
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data.
DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation
Concretely, we develop a multi-scale regional feature generator that can generate multiple spatial context-aware representations from pre-trained deep convolutional networks for every subregion of an image.
Ranked #67 on
Anomaly Detection
on MVTec AD
Learning to Incorporate Structure Knowledge for Image Inpainting
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works.
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES
Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task.
s-LWSR: Super Lightweight Super-Resolution Network
Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance.
Learning from Label Proportions with Generative Adversarial Networks
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available.
Automatic Road Crack Detection Using Random Structured Forests
Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high- performance crack detector, which can identify arbitrarily com- plex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively.
