Results 1 to 10 of about 9,677,595 (330)
Residual Attention Network for Image Classification [PDF]
Computer Vision and Pattern Recognition, 2017 In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is
Jiang, Mengqing +7 more
core +2 more sources
Dataset for image classification with knowledge. [PDF]
Data BriefDeep learning applied to raw data has demonstrated outstanding image classification performance, mainly when abundant data is available. However, performance significantly degrades when a substantial volume of data is unavailable. Furthermore, deep architectures struggle to achieve satisfactory performance levels when distinguishing between distinct ...
Mbiaya FA +4 more
europepmc +4 more sources
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification [PDF]
IEEE International Conference on Computer Vision, 2021 The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. Inspired by this, in this paper, we study how to learn multi-scale feature representations in transformer ...
Chun-Fu Chen, Quanfu Fan, Rameswar Panda
semanticscholar +1 more source
SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2021 Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.
D. Hong +6 more
semanticscholar +1 more source
ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which image patches interact, independently and identically across channels ...
Hugo Touvron +10 more
semanticscholar +1 more source
MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification [PDF]
Scientific Data, 2021 Measurement(s) supervised machine learning Technology Type(s) machine learning We introduce MedMNIST v2 , a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.
Jiancheng Yang +7 more
semanticscholar +1 more source
Imaging Classification of Constipation
Journal of the Anus, Rectum and Colon, 2023 The diagnosis of patients with chronic constipation is very complicated. This study aimed to develop a simple imaging classification for the diagnosis of chronic constipation by abdominal computed tomography (CT).Sixty-two patients who underwent abdominal CT in our hospital between January and June 2022 were enrolled.
Kei Ishimaru +11 more
openaire +3 more sources
Quantum machine learning for image classification [PDF]
Machine Learning: Science and Technology, 2023 Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the ...
Arsenii Senokosov +4 more
semanticscholar +1 more source
Big Self-Supervised Models Advance Medical Image Classification [PDF]
IEEE International Conference on Computer Vision, 2021 Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis.
Shekoofeh Azizi +11 more
semanticscholar +1 more source
Eigenregions for image classification [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004 For certain databases and classification tasks, analyzing images based region features instead of image features results in more accurate classifications. We introduce eigenregions, which are geometrical features that encompass area, location, and shape properties of an image region, even if the region is spatially incoherent.
Sabine Süsstrunk +2 more
openaire +2 more sources