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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8101–8150 of 10420 papers

TitleStatusHype
SELF: Learning to Filter Noisy Labels with Self-Ensembling—0
Generating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image ClassificationCode0
Training Multiscale-CNN for Large Microscopy Image Classification in One Hour—0
BUZz: BUffer Zones for defending adversarial examples in image classification—0
Generalization Bounds for Convolutional Neural Networks—0
An empirical study of pretrained representations for few-shot classification—0
A General Upper Bound for Unsupervised Domain Adaptation—0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
Contextual Local Explanation for Black Box Classifiers—0
W-Net: A CNN-based Architecture for White Blood Cells Image ClassificationCode0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time SystemsCode1
A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision—0
DANet: Divergent Activation for Weakly Supervised Object LocalizationCode0
Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification—0
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos—0
Global Feature Guided Local Pooling—0
Siamese Networks: The Tale of Two Manifolds—0
Scalable Verified Training for Provably Robust Image Classification—0
A Large-scale Study of Representation Learning with the Visual Task Adaptation BenchmarkCode0
Addressing Failure Prediction by Learning Model ConfidenceCode1
Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence—0
Distilling Effective Supervision from Severe Label NoiseCode0
SlowMo: Improving Communication-Efficient Distributed SGD with Slow MomentumCode0
Augmenting learning using symmetry in a biologically-inspired domain—0
Leveraging Model Interpretability and Stability to increase Model RobustnessCode0
Gated Linear NetworksCode0
Meta-learning algorithms for Few-Shot Computer VisionCode0
Hidden Trigger Backdoor AttacksCode1
RandAugment: Practical automated data augmentation with a reduced search spaceCode2
XNOR-Net++: Improved Binary Neural Networks—0
Fusion of Convolutional Neural Network and Statistical Features for Texture classification—0
Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional NetworkCode1
Test-Time Training with Self-Supervision for Generalization under Distribution ShiftsCode0
Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image ClassificationCode0
Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image ClassificationCode0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks—0
Interpreting Undesirable Pixels for Image Classification on Black-Box Models—0
Urban Sound Tagging using Convolutional Neural NetworksCode0
Adaptive Binary-Ternary Quantization—0
Two-stage Image Classification Supervised by a Single Teacher Single Student ModelCode0
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network—0
Balanced Binary Neural Networks with Gated ResidualCode0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
Adapting to Label Shift with Bias-Corrected Calibration—0
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS—0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS—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