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 4001–4025 of 10420 papers

TitleStatusHype
MABViT -- Modified Attention Block Enhances Vision Transformers—0
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation—0
Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines—0
A Comprehensive Study of Vision Transformers in Image Classification Tasks—0
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer—0
Improving Normalization with the James-Stein Estimator—0
SCHEME: Scalable Channel Mixer for Vision Transformers—0
Physics Inspired Criterion for Pruning-Quantization Joint LearningCode0
PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight PredictionCode0
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—0
Developmental Pretraining (DPT) for Image Classification NetworksCode0
Benchmarking Multi-Domain Active Learning on Image Classification—0
CLIP-QDA: An Explainable Concept Bottleneck Model—0
Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation—0
Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications—0
Negotiated Representations to Prevent Forgetting in Machine Learning ApplicationsCode0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters—0
Rethinking Image Editing Detection in the Era of Generative AI Revolution—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Enhancing Post-Hoc Explanation Benchmark Reliability for Image Classification—0
LayerCollapse: Adaptive compression of neural networks—0
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans—0
Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels—0
Compressing the Backward Pass of Large-Scale Neural Architectures by Structured Activation Pruning—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