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 526–550 of 10419 papers

TitleStatusHype
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI PoolingCode1
ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual ConnectionsCode1
Switch EMA: A Free Lunch for Better Flatness and SharpnessCode1
NOAH: Learning Pairwise Object Category Attentions for Image ClassificationCode1
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
Dendritic Learning-incorporated Vision Transformer for Image RecognitionCode1
A Single Graph Convolution Is All You Need: Efficient Grayscale Image ClassificationCode1
Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial LabelsCode1
Revisiting Active Learning in the Era of Vision Foundation ModelsCode1
Rethinking Centered Kernel Alignment in Knowledge DistillationCode1
PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as GraphsCode1
Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple ModalitiesCode1
Do Vision and Language Encoders Represent the World Similarly?Code1
Prompt-driven Latent Domain Generalization for Medical Image ClassificationCode1
Improved Zero-Shot Classification by Adapting VLMs with Text DescriptionsCode1
Transductive Zero-Shot and Few-Shot CLIPCode1
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity TrainingCode1
DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge GraphsCode1
Federated Learning via Input-Output Collaborative DistillationCode1
Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object DetectionCode1
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
Cached Transformers: Improving Transformers with Differentiable Memory CacheCode1
Adversarial AutoMixupCode1
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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