SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

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Showing 6501–6550 of 10420 papers

TitleStatusHype
Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches—0
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms—0
Lean classical-quantum hybrid neural network model for image classification—0
2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks—0
Learning efficient structured dictionary for image classification—0
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks—0
m-RevNet: Deep Reversible Neural Networks with Momentum—0
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging—0
Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding—0
Learning Disentangled Representations of Satellite Image Time Series—0
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification—0
Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework—0
BenthIQ: a Transformer-Based Benthic Classification Model for Coral Restoration—0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification—0
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification—0
MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis—0
Learning Discriminative Features Via Weights-biased Softmax Loss—0
Learning Dependency Structures for Weak Supervision Models—0
Learning degraded image classification with restoration data fidelity—0
Delving Deeper Into Astromorphic Transformers—0
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint—0
Learning Deep Optimal Embeddings with Sinkhorn Divergences—0
Learning Deep NBNN Representations for Robust Place Categorization—0
MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900-2015)—0
Analysis of Convolutional Neural Networks for Document Image Classification—0
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans—0
Learning Deep Context-Network Architectures for Image Annotation—0
MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception Networks—0
Delay Differential Neural Networks—0
Learning Data Teaching Strategies Via Knowledge Tracing—0
Déjà Vu Memorization in Vision-Language Models—0
Benchmarks for Image Classification and Other High-dimensional Pattern Recognition Problems—0
Benchmarking the Robustness of Semantic Segmentation Models—0
Analysis of convolutional neural network image classifiers in a rotationally symmetric model—0
Learning cross space mapping via DNN using large scale click-through logs—0
Multi-branch fusion network for hyperspectral image classification—0
Learning Cross-domain Generalizable Features by Representation Disentanglement—0
Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis—0
Learning Continually from Low-shot Data Stream—0
Multiclass Burn Wound Image Classification Using Deep Convolutional Neural Networks—0
Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks—0
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints—0
Learning Consistent Deep Generative Models from Sparsely Labeled Data—0
Deformable Gabor Feature Networks for Biomedical Image Classification—0
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices—0
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling—0
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks—0
Learning Connectivity of Neural Networks from a Topological Perspective—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified