Papers 3D Classification
“3D Classification” 태그가 달린 논문 91편 · 필터 해제
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network
In this paper, we investigate the dynamics-aware adversarial attack problem in deep neural networks. Most existing adversarial attack algorithms are designed under a basic assumption -- the network architecture is fixed …
3D Classification3D Semantic SegmentationAdversarial AttackComputational EfficiencyVoint Cloud: Multi-View Point Cloud Representation for 3D Understanding
Multi-view projection methods have demonstrated promising performance on 3D understanding tasks like 3D classification and segmentation. However, it remains unclear how to combine such multi-view methods with the widely …
3D Classification3D Part Segmentation3D Semantic SegmentationRetrieval+2Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours
Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipeline. However, training accurate and reliable CNNs requires large fine-grain annotated da…
3D ClassificationDiagnosticSuperpixelsWeakly supervised segmentationDeep Learning Based Automated COVID-19 Classification from Computed Tomography Images
A method of a Convolutional Neural Networks (CNN) for image classification with image preprocessing and hyperparameters tuning was proposed. The method aims at increasing the predictive performance for COVID-19 diagnosis…
3D ClassificationAnatomyClassificationCOVID-19 Diagnosis+2Multimodal Semi-Supervised Learning for 3D Objects
In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D tasks due to the scarcity of labeled 3D data…
3D ClassificationRetrievalDynamic Local Geometry Capture in 3D PointCloud Classification
With the advent of PointNet, the popularity of deep neural networks has increased in point cloud analysis. PointNet's successor, PointNet++, partitions the input point cloud and recursively applies PointNet to capture lo…
3D Classification3D Point Cloud ClassificationClassificationLearning Inner-Group Relations on Point Clouds
The prevalence of relation networks in computer vision is in stark contrast to underexplored point-based methods. In this paper, we explore the possibilities of local relation operators and survey their feasibility. We p…
3D Classification3D Point Cloud Classification3D Semantic SegmentationClassification+3Subdivision-Based Mesh Convolution Networks
Convolutional neural networks (CNNs) have made great breakthroughs in 2D computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provid…
3D ClassificationPose EstimationVA-GCN: A Vector Attention Graph Convolution Network for learning on Point Clouds
Owing to the development of research on local aggregation operators, dramatic breakthrough has been made in point cloud analysis models. However, existing local aggregation operators in the current literature fail to att…
3D ClassificationConvolutional neural networks for Alzheimer’s disease detection on MRI images
Purpose: Detection of Alzheimer’s disease (AD) on magnetic resonance imaging (MRI) using convolutional neural networks (CNNs), which is useful for detecting AD in its preliminary states. Approach: Our study implements…
3D ClassificationAlzheimer's Disease DetectionMedical DiagnosisSpecificity+1Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks
We propose simple yet effective improvements in point representations and local neighborhood graph construction within the general framework of graph neural networks (GNNs) for 3D point cloud processing. As a first contr…
3D Classification3D Instance Segmentation3D Part Segmentation3D Point Cloud Classification+3Concentric Spherical GNN for 3D Representation Learning
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resol…
3D ClassificationRepresentation LearningThe Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network
This paper investigates the design of time and memory efficient graph convolutional networks (GCNs). State-of-the-art GCNs adopt $K$-nearest neighbor (KNN) searches for local feature aggregation and feature extraction op…
3D ClassificationPoint Cloud ClassificationSegmentationSemantic SegmentationDeep manifold learning reveals hidden dynamics of proteasome autoregulation
The 2.5-MDa 26S proteasome maintains proteostasis and regulates myriad cellular processes. How polyubiquitylated substrate interactions regulate proteasome activity is not understood. Here we introduce a deep manifold le…
3D ClassificationClusteringCryogenic Electron Microscopy (cryo-EM)Task-Oriented Feature Distillation
Feature distillation, a primary method in knowledge distillation, always leads to significant accuracy improvements. Most existing methods distill features in the teacher network through a manually designed transformatio…
3D ClassificationGeneral Classificationimage-classificationImage Classification+1MVTN: Multi-View Transformation Network for 3D Shape Recognition
Multi-view projection methods have demonstrated their ability to reach state-of-the-art performance on 3D shape recognition. Those methods learn different ways to aggregate information from multiple views. However, the c…
3D Classification3D Object Retrieval3D Point Cloud Classification3D Shape Classification+4Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features
Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of negative slices along multiple planes. F…
3D ClassificationBinary ClassificationClassificationGeneral Classification+2Cross-Modality 3D Object Detection
In this paper, we focus on exploring the fusion of images and point clouds for 3D object detection in view of the complementary nature of the two modalities, i.e., images possess more semantic information while point clo…
3D Classification3D Object DetectionData AugmentationObject+3Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks
We propose novel approaches for simultaneously identifying important weights of a convolutional neural network (ConvNet) and providing more attention to the important weights during training. More formally, we identify t…
3D Action Recognition3D Classification3D Object Classification3D Semantic Segmentation+9MeshWalker: Deep Mesh Understanding by Random Walks
Most attempts to represent 3D shapes for deep learning have focused on volumetric grids, multi-view images and point clouds. In this paper we look at the most popular representation of 3D shapes in computer graphics - a …
3D Classification3D Object RecognitionSemantic Segmentation