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 8451–8500 of 10420 papers

TitleStatusHype
Hierarchical Auxiliary Learning—0
Deeply-supervised Knowledge SynergyCode0
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training—0
Adversarial Examples for Edge Detection: They Exist, and They Transfer—0
Detecting Anomalies in Image Classification by Means of Semantic RelationshipsCode0
ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering—0
Exploration of Noise Strategies in Semi-supervised Named Entity Classification—0
Perceptual Evaluation of Adversarial Attacks for CNN-based Image ClassificationCode0
GANchors: Realistic Image Perturbation Distributions for Anchors Using Generative ModelsCode0
Weakly Supervised Image Classification Through Noise Regularization—0
Transferable AutoML by Model Sharing Over Grouped Datasets—0
Versatile Multiple Choice Learning and Its Application to Vision Computing—0
Structured Knowledge Distillation for Semantic SegmentationCode0
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification—0
Visual Attention Consistency Under Image Transforms for Multi-Label Image ClassificationCode0
Not Using the Car to See the Sidewalk -- Quantifying and Controlling the Effects of Context in Classification and Segmentation—0
Embedding Complementary Deep Networks for Image Classification—0
Adapting Object Detectors via Selective Cross-Domain AlignmentCode0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Networks—0
Compact Feature Learning for Multi-Domain Image Classification—0
Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar PhysicsCode0
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1—0
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial RobustnessCode0
Characterizing Bias in Classifiers using Generative ModelsCode0
P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification—0
Deep multi-class learning from label proportions—0
Toward Runtime-Throttleable Neural Networks—0
Empirically Measuring Concentration: Fundamental Limits on Intrinsic RobustnessCode0
An Inertial Newton Algorithm for Deep LearningCode0
Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets—0
A Study of BFLOAT16 for Deep Learning Training—0
Training Data Subset Search with Ensemble Active Learning—0
Image classification using quantum inference on the D-Wave 2X—0
Bayesian Nonparametric Federated Learning of Neural NetworksCode0
Differential Privacy Has Disparate Impact on Model AccuracyCode0
Inference with Hybrid Bio-hardware Neural Networks—0
CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural NetworksCode0
Texture CNN for Thermoelectric Metal Pipe Image Classification—0
Texture CNN for Histopathological Image Classification—0
A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections—0
Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion—0
Efficient Object Embedding for Spliced Image Retrieval—0
Why gradient clipping accelerates training: A theoretical justification for adaptivityCode0
RecNets: Channel-wise Recurrent Convolutional Neural Networks—0
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
Network DeconvolutionCode0
Capsule Routing via Variational BayesCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
Combating Label Noise in Deep Learning Using AbstentionCode1
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural NetworksCode0
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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