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 1776–1800 of 10420 papers

TitleStatusHype
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image ClassificationCode0
Debiased Noise Editing on Foundation Models for Fair Medical Image ClassificationCode0
Probing Image Compression For Class-Incremental Learning—0
Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification—0
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape—0
Frequency Attention for Knowledge DistillationCode1
Multiple Instance Learning with random sampling for Whole Slide Image Classification—0
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation—0
Tune without Validation: Searching for Learning Rate and Weight Decay on Training Sets—0
Feature CAM: Interpretable AI in Image Classification—0
Defending Against Unforeseen Failure Modes with Latent Adversarial TrainingCode1
ComFe: Interpretable Image Classifiers With Foundation Models, Transformers and Component FeaturesCode0
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks—0
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning—0
MedMamba: Vision Mamba for Medical Image ClassificationCode4
On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained EncoderCode0
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN—0
A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural NetworkCode1
G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth—0
Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use—0
SGD with Partial Hessian for Deep Neural Networks OptimizationCode0
SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix—0
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGradCode0
Trainable Fractional Fourier TransformCode2
Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like ArchitecturesCode4
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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