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

Showing 83518400 of 10420 papers

TitleStatusHype
MACE: Model Agnostic Concept Extractor for Explaining Image Classification NetworksCode0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Automatic Open-World Reliability AssessmentCode0
Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label SmoothingCode0
Gall Bladder Cancer Detection from US Images with Only Image Level LabelsCode0
Evolving Deep Convolutional Neural Networks for Image ClassificationCode0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
Evolutionary NAS with Gene Expression Programming of Cellular EncodingCode0
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGradCode0
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning RatesCode0
On the Improvement of Generalization and Stability of Forward-Only Learning via Neural PolarizationCode0
Removing the Feature Correlation Effect of Multiplicative NoiseCode0
Evaluation of Output Embeddings for Fine-Grained Image ClassificationCode0
Gated Channel Transformation for Visual RecognitionCode0
Gated Convolutional Networks with Hybrid Connectivity for Image ClassificationCode0
Gated Linear NetworksCode0
Gated Recurrent Convolution Neural Network for OCRCode0
ReNet: A Recurrent Neural Network Based Alternative to Convolutional NetworksCode0
Evaluation of Explanation Methods of AI -- CNNs in Image Classification Tasks with Reference-based and No-reference MetricsCode0
Gaussian-Based Pooling for Convolutional Neural NetworksCode0
Machine Learning Models that Remember Too MuchCode0
Machine Learning State-of-the-Art with UncertaintiesCode0
Evaluation and Comparison of Visual Language Models for Transportation Engineering ProblemsCode0
Anchor Loss: Modulating Loss Scale based on Prediction DifficultyCode0
Evaluating the Performance of TAAF for image classification modelsCode0
Gaze-Guided Learning: Avoiding Shortcut Bias in Visual ClassificationCode0
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
An Automated Ensemble Learning Framework Using Genetic Programming for Image ClassificationCode0
On-the-Job Learning with Bayesian Decision TheoryCode0
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image ModelsCode0
GDN: A Stacking Network Used for Skin Cancer DiagnosisCode0
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion ReductionCode0
RepAct: The Re-parameterizable Adaptive Activation FunctionCode0
MAD-VAE: Manifold Awareness Defense Variational AutoencoderCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays OffCode0
General Greedy De-bias LearningCode0
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicesCode0
Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale EmbeddingsCode0
On the notion of number in humans and machinesCode0
Evaluating Supervision Levels Trade-Offs for Infrared-Based People CountingCode0
PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image ClassificationCode0
Automatic location detection based on deep learningCode0
An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image ClassificationCode0
On the Practicality of Deterministic Epistemic UncertaintyCode0
Correcting the Triplet Selection Bias for Triplet LossCode0
PSDPM: Prototype-based Secondary Discriminative Pixels Mining for Weakly Supervised Semantic SegmentationCode0
CoRLD: Contrastive Representation Learning Of Deformable Shapes In ImagesCode0
Making Better Mistakes: Leveraging Class Hierarchies with Deep NetworksCode0
Robust Collaborative Learning with Linear Gradient OverheadCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified