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 1851–1900 of 10419 papers

TitleStatusHype
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods—0
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning—0
Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image ClassificationCode0
Distilled Gradual Pruning with Pruned Fine-tuningCode0
What to Do When Your Discrete Optimization Is the Size of a Neural Network?Code0
Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification—0
ViGEO: an Assessment of Vision GNNs in Earth ObservationCode0
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment—0
Balancing the Causal Effects in Class-Incremental Learning—0
How Flawed Is ECE? An Analysis via Logit SmoothingCode0
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing DiffusionCode0
I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption—0
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lagCode0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models—0
Switch EMA: A Free Lunch for Better Flatness and SharpnessCode1
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs—0
APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks—0
Contrastive Learning for Regression on Hyperspectral Data—0
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging ClassificationCode0
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensembleCode0
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense—0
A novel spatial-frequency domain network for zero-shot incremental learning—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
Latent Enhancing AutoEncoder for Occluded Image Classification—0
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks—0
SAE: Single Architecture Ensemble Neural Networks—0
The SkipSponge Attack: Sponge Weight Poisoning of Deep Neural Networks—0
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows—0
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive SurveyCode3
Adaptive Activation Functions for Predictive Modeling with Sparse Experimental DataCode0
Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors—0
Multi-Scale Semantic Segmentation with Modified MBConv Blocks—0
A Lightweight Randomized Nonlinear Dictionary Learning Method using Random Vector Functional Link—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
EVA-CLIP-18B: Scaling CLIP to 18 Billion ParametersCode0
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained WarpingCode0
Pre-training of Lightweight Vision Transformers on Small Datasets with Minimally Scaled Images—0
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
SynthVision - Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data—0
NOAH: Learning Pairwise Object Category Attentions for Image ClassificationCode1
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active LearningCode0
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision TransformersCode0
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning—0
Déjà Vu Memorization in Vision-Language Models—0
CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors—0
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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