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 2551–2575 of 10420 papers

TitleStatusHype
Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars—0
A 3-stage Spectral-spatial Method for Hyperspectral Image Classification—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
Applications of the Streaming Networks—0
CLIP-QDA: An Explainable Concept Bottleneck Model—0
Applications of Sequential Learning for Medical Image Classification—0
Dual adaptive training of photonic neural networks—0
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification—0
Dynamically Stable Poincaré Embeddings for Neural Manifolds—0
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement—0
Application of the Neural Network Dependability Kit in Real-World Environments—0
Adversarial Examples in Remote Sensing—0
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP—0
Application of the Modified Fractal Signature Method for Terrain Classification from Synthetic Aperture Radar Images—0
CLIPAG: Towards Generator-Free Text-to-Image Generation—0
Application of Sensitivity Analysis Methods for Studying Neural Network Models—0
DSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
A New Quantum CNN Model for Image Classification—0
ClickBAIT-v2: Training an Object Detector in Real-Time—0
A 3D 2D convolutional Neural Network Model for Hyperspectral Image Classification—0
Vera Verto: Multimodal Hijacking Attack—0
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks—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