Papers 3D Classification
“3D Classification” 태그가 달린 논문 91편 · 필터 해제
Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data
Semi-supervised learning (SSL) has shown its effectiveness in learning effective 3D representation from a small amount of labelled data while utilizing large unlabelled data. Traditional semi-supervised approaches rely o…
3D Classification3D Object RecognitionComputer Vision Intelligence Test Modeling and Generation: A Case Study on Smart OCR
AI-based systems possess distinctive characteristics and introduce challenges in quality evaluation at the same time. Consequently, ensuring and validating AI software quality is of critical importance. In this paper, we…
3D ClassificationOptical Character RecognitionOptical Character Recognition (OCR)software testingConDense: Consistent 2D/3D Pre-training for Dense and Sparse Features from Multi-View Images
To advance the state of the art in the creation of 3D foundation models, this paper introduces the ConDense framework for 3D pre-training utilizing existing pre-trained 2D networks and large-scale multi-view datasets. We…
3D ClassificationNeRFP3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders
3D pre-training is crucial to 3D perception tasks. Nevertheless, limited by the difficulties in collecting clean and complete 3D data, 3D pre-training has persistently faced data scaling challenges. In this work, we intr…
3D Classification3D ReconstructionDepth EstimationFew-Shot LearningAXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans
This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extra…
3D ClassificationAlzheimer's Disease DetectionBinary ClassificationExplainable Artificial Intelligence (XAI)+3Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks
The manifestation of symptoms associated with lung diseases can vary in different depths for individual patients, highlighting the significance of 3D information in CT scans for medical image classification. While Vision…
3D ClassificationClassificationimage-classificationImage Classification+1Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision
Parameter-efficient fine-tuning (PEFT) has become increasingly important as foundation models continue to grow in both popularity and size. Adapter has been particularly well-received due to their potential for parameter…
3D Classificationparameter-efficient fine-tuningNo Captions, No Problem: Captionless 3D-CLIP Alignment with Hard Negatives via CLIP Knowledge and LLMs
In this study, we explore an alternative approach to enhance contrastive text-image-3D alignment in the absence of textual descriptions for 3D objects. We introduce two unsupervised methods, $I2I$ and $(I2L)^2$, which le…
3D ClassificationCross-Modal RetrievalImage RetrievalRetrievalCross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation
Medical image analysis suffers from a shortage of data, whether annotated or not. This becomes even more pronounced when it comes to 3D medical images. Self-Supervised Learning (SSL) can partially ease this situation by …
3D ClassificationMedical Image AnalysisRepresentation LearningSelf-Supervised LearningMM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding
We introduce MM-Mixing, a multi-modal mixing alignment framework for 3D understanding. MM-Mixing applies mixing-based methods to multi-modal data, preserving and optimizing cross-modal connections while enhancing diversi…
3D Classification3D Object Recognition3D Shape RetrievalContrastive Learning+6SE3D: A Framework For Saliency Method Evaluation In 3D Imaging
For more than a decade, deep learning models have been dominating in various 2D imaging tasks. Their application is now extending to 3D imaging, with 3D Convolutional Neural Networks (3D CNNs) being able to process LIDAR…
3D Classification3D Object Detection3D Semantic SegmentationAutonomous Driving+3Deep Models for Multi-View 3D Object Recognition: A Review
Human decision-making often relies on visual information from multiple perspectives or views. In contrast, machine learning-based object recognition utilizes information from a single image of the object. However, the in…
3D Classification3D Object RecognitionDecision MakingObject+2Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
Multi-parametric MRI of the body is routinely acquired for the identification of abnormalities and diagnosis of diseases. However, a standard naming convention for the MRI protocols and associated sequences does not exis…
3D ClassificationGroup Multi-View Transformer for 3D Shape Analysis with Spatial Encoding
In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address …
3D Classification3D Shape RecognitionKnowledge DistillationRetrievalMV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition
Large-scale pre-trained models have demonstrated impressive performance in vision and language tasks within open-world scenarios. Due to the lack of comparable pre-trained models for 3D shapes, recent methods utilize lan…
3D Classification3D Shape RecognitionZero-shot 3D classificationAdvancements in Point Cloud Data Augmentation for Deep Learning: A Survey
Deep learning (DL) has become one of the mainstream and effective methods for point cloud analysis tasks such as detection, segmentation and classification. To reduce overfitting during training DL models and improve mod…
3D Classification3D Object DetectionAutonomous DrivingData Augmentation+2ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights
Though the success of CLIP-based training recipes in vision-language models, their scalability to more modalities (e.g., 3D, audio, etc.) is limited to large-scale data, which is expensive or even inapplicable for rare m…
3D ClassificationQuestion AnsweringRepresentation LearningTraining-free 3D Point Cloud Classification+2Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging
The credibility of Artificial Intelligence (AI) models in medical imaging, particularly during the COVID-19 pandemic, has been challenged by reproducibility issues and obscured clinical insights. To address these concern…
3D ClassificationComputed Tomography (CT)COVID-19 DiagnosisRobustifying Point Cloud Networks by Refocusing
The ability to cope with out-of-distribution (OOD) corruptions and adversarial attacks is crucial in real-world safety-demanding applications. In this study, we develop a general mechanism to increase neural network robu…
3D ClassificationAdversarial DefensePoint Cloud ClassificationRobust classification+2Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement
Due to the extremely low signal-to-noise ratio (SNR) and unknown poses (projection angles and image shifts) in cryo-electron microscopy (cryo-EM) experiments, reconstructing 3D volumes from 2D images is very challenging.…
3D Classification3D ReconstructionDisentanglementPose Estimation+1