472 Result(s)
within
Image Processing and Computer Vision
Yi**ang Chen
Chapter
Conference Paper
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Chapter and Conference Paper
Towards Real-Time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-Decoder Architecture
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation varies from image to image. Recent methods adopt deep neural networks to ...
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Chapter and Conference Paper
Ethical Concerns of COVID-19 Contact Tracing: A Narrative Review
Contact tracing has been widely adopted during COVID-19 to curb the spread of infection. Despite its effectiveness, ethical issues abound and many people are not willing to use it. Toward understanding the eth...
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Chapter and Conference Paper
MCSketch: An Accurate Sketch for Heavy Flow Detection and Heavy Flow Frequency Estimation
Accurately finding heavy flows in data streams is challenging owing to limited memory availability. Prior algorithms have focused on accuracy in heavy flow detection but cannot provide the frequency of a heavy...
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Chapter and Conference Paper
Object Centric Point Sets Feature Learning with Matrix Decomposition
A representation matching the invariance/equivariance characteristics must be learnt to rebuild a morphable 3D model from a single picture input. However, present approaches for dealing with 3D point clouds de...
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Chapter and Conference Paper
Fake News Detection Based on the Correlation Extension of Multimodal Information
Online social media is characterized by a large number of users that creates conditions for large-scale news generation. News in multimodal form (images and text) often has a serious negative impact. Existing ...
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Chapter and Conference Paper
Unsupervised Deep Transfer Learning Model for Tool Wear States Recognition
Heavy worn tools can cause severe cutting vibrations, leading to a decrease in the surface quality of the workpiece. It is important to monitor tool states and replace the worn tool in time. The traditional to...
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Chapter and Conference Paper
UnconFuse: Avatar Reconstruction from Unconstrained Images
The report proposes an effective solution about 3D human body reconstruction from multiple unconstrained frames for ECCV 2022 WCPA Challenge: From Face, Body and Fashion to 3D Virtual avatars I (track1: Multi-...
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Chapter and Conference Paper
Gift from Nature: Potential Energy Minimization for Explainable Dataset Distillation
Dataset distillation aims to reduce the dataset size by capturing important information from original dataset. It can significantly improve the feature extraction effectiveness, storage efficiency and training...
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Chapter and Conference Paper
OVPT: Optimal Viewset Pooling Transformer for 3D Object Recognition
The current methods for multi-view-based 3D object recognition have the problem of losing the correlation between views and rendering 3D objects with multi-view redundancy. This makes it difficult to improve r...
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Chapter and Conference Paper
Robust Human Matting via Semantic Guidance
Automatic human matting is highly desired for many real applications. We investigate recent human matting methods and show that common bad cases happen when semantic human segmentation fails. This indicates th...
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Chapter and Conference Paper
Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation
Existing spectral reconstruction methods learn discrete map**s from spectrally downsampled measurements (e.g., RGB images) to a specific number of spectral bands. However, they generally neglect the continuo...
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Chapter and Conference Paper
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level vision tasks operate in the R...
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Chapter and Conference Paper
SpMVNet: Spatial Multi-view Network for Head and Neck Organs at Risk Segmentation
Head and neck (HaN) cancers are often treated with radiotherapy. Since radiation inevitably causes damage to human organs, it is necessary to control the dose of radiation in different areas during radiation t...
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Chapter and Conference Paper
An Adaptive Clustering Approach for Efficient Data Dissemination in IoV
Due to the inherent characteristics of Vehicular Ad-hoc Networks (VANETs), such as uneven distribution and high mobility, establishing and maintaining efficient routes for data dissemination is a significant a...
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Chapter and Conference Paper
Perceiving and Modeling Density for Image Dehazing
In the real world, the degradation of images taken under haze can be quite complex, where the spatial distribution of haze varies from image to image. Recent methods adopt deep neural networks to recover clean...
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Chapter and Conference Paper
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo
Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural inverse rendering method ...
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Chapter and Conference Paper
RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning
Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision. Several algorithms have been developed and obtained considerable success. Ho...
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Chapter and Conference Paper
\(\text {Face2Face}^\rho \) : Real-Time High-Resolution One-Shot Face Reenactment
Existing one-shot face reenactment methods either present obvious artifacts in large pose transformations, or cannot well-preserve the identity information in the source images, or fail to meet the requirement...
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Chapter and Conference Paper
When Active Learning Meets Implicit Semantic Data Augmentation
Active learning (AL) is a label-efficient technique for training deep models when only a limited labeled set is available and the manual annotation is expensive. Implicit semantic data augmentation (ISDA) effe...
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Chapter and Conference Paper
A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation
This work presents a simple vision transformer design as a strong baseline for object localization and instance segmentation tasks. Transformers recently demonstrate competitive performance in image classifica...
