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 13011350 of 10419 papers

TitleStatusHype
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
ML-Decoder: Scalable and Versatile Classification HeadCode1
PeCo: Perceptual Codebook for BERT Pre-training of Vision TransformersCode1
Sharpness-aware Quantization for Deep Neural NetworksCode1
Focal and Global Knowledge Distillation for DetectorsCode1
AutoDC: Automated data-centric processingCode1
Florence: A New Foundation Model for Computer VisionCode1
Semi-Supervised Vision TransformersCode1
SSR: An Efficient and Robust Framework for Learning with Unknown Label NoiseCode1
FedCV: A Federated Learning Framework for Diverse Computer Vision TasksCode1
Grounded Situation Recognition with TransformersCode1
Swin Transformer V2: Scaling Up Capacity and ResolutionCode1
Benchmarking and scaling of deep learning models for land cover image classificationCode1
Detecting AutoAttack Perturbations in the Frequency DomainCode1
iBOT: Image BERT Pre-Training with Online TokenizerCode1
LiT: Zero-Shot Transfer with Locked-image text TuningCode1
Masked Autoencoders Are Scalable Vision LearnersCode1
Probabilistic Contrastive Learning for Domain AdaptationCode1
Keys to Accurate Feature Extraction Using Residual Spiking Neural NetworksCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Sliced Recursive TransformerCode1
Improved Regularization and Robustness for Fine-tuning in Neural NetworksCode1
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary DataCode1
FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel RepresentationCode1
Arch-Net: Model Distillation for Architecture Agnostic Model DeploymentCode1
Multi-Scale High-Resolution Vision Transformer for Semantic SegmentationCode1
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?Code1
Explaining Latent Representations with a Corpus of ExamplesCode1
MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep LearningCode1
Physically Explainable CNN for SAR Image ClassificationCode1
Towards artificial general intelligence via a multimodal foundation modelCode1
On sensitivity of meta-learning to support dataCode1
Stable Anderson Acceleration for Deep LearningCode1
ZerO Initialization: Initializing Neural Networks with only Zeros and OnesCode1
Instance-Conditional Knowledge Distillation for Object DetectionCode1
MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited LabelsCode1
Sinkformers: Transformers with Doubly Stochastic AttentionCode1
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer AggregationCode1
A Simple Baseline for Low-Budget Active LearningCode1
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image ClassificationCode1
Learning Partial Equivariances from DataCode1
Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic OptimizationCode1
HRFormer: High-Resolution Transformer for Dense PredictionCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated BranchesCode1
Self-Supervised Learning by Estimating Twin Class DistributionsCode1
FocusNet: Classifying Better by Focusing on Confusing ClassesCode1
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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