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 1051–1100 of 10419 papers

TitleStatusHype
Beyond Categorical Label Representations for Image ClassificationCode1
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image ClassificationCode1
Dual-stage Hyperspectral Image Classification Model with Spectral SupertokenCode1
Dynamic Defense Against Byzantine Poisoning Attacks in Federated LearningCode1
DPMLBench: Holistic Evaluation of Differentially Private Machine LearningCode1
Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNetCode1
DPT: Deformable Patch-based Transformer for Visual RecognitionCode1
Better plain ViT baselines for ImageNet-1kCode1
Conditional Positional Encodings for Vision TransformersCode1
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharingCode1
Do Vision Transformers See Like Convolutional Neural Networks?Code1
Do Vision and Language Encoders Represent the World Similarly?Code1
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse TrainingCode1
Domain Generalization via Gradient SurgeryCode1
Benchmarking Test-Time Adaptation against Distribution Shifts in Image ClassificationCode1
Do text-free diffusion models learn discriminative visual representations?Code1
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?Code1
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic CalibrationCode1
Dynamic Grained Encoder for Vision TransformersCode1
Benchmarking Adversarial Robustness on Image ClassificationCode1
Do Input Gradients Highlight Discriminative Features?Code1
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness MetricsCode1
A Less Biased Evaluation of Out-of-distribution Sample DetectorsCode1
Domain Adaptation for Multi-label Image Classification: a Discriminator-free ApproachCode1
BCN: Batch Channel Normalization for Image ClassificationCode1
DocXClassifier: High Performance Explainable Deep Network for Document Image ClassificationCode1
Do Deep Networks Transfer Invariances Across Classes?Code1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Making Convolutional Networks Shift-Invariant AgainCode1
DO-Conv: Depthwise Over-parameterized Convolutional LayerCode1
Does VLM Classification Benefit from LLM Description Semantics?Code1
Domain-Adversarial Training of Neural NetworksCode1
AIDeveloper: deep learning image classification in life science and beyondCode1
Bayesian Optimization Meets Self-DistillationCode1
DKDFN: Domain Knowledge-Guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classificationCode1
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural NetworksCode1
DLME: Deep Local-flatness Manifold EmbeddingCode1
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical ImagesCode1
Bayesian Model-Agnostic Meta-LearningCode1
Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image ClassificationCode1
Bayesian Neural Network Priors RevisitedCode1
Bayesian continual learning and forgetting in neural networksCode1
ActMAD: Activation Matching to Align Distributions for Test-Time-TrainingCode1
Boosting Co-teaching with Compression Regularization for Label NoiseCode1
DMT-JEPA: Discriminative Masked Targets for Joint-Embedding Predictive ArchitectureCode1
Domain Generalization by Learning and Removing Domain-specific FeaturesCode1
Dynamic Graph Message Passing NetworksCode1
batchboost: regularization for stabilizing training with resistance to underfitting & overfittingCode1
Divergences in Color Perception between Deep Neural Networks and HumansCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
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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