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 5626–5650 of 10420 papers

TitleStatusHype
LEAP: Learning Embeddings for Adaptive Pace—0
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups—0
Learnable Bernoulli Dropout for Bayesian Deep Learning—0
Light Lies: Optical Adversarial Attack—0
Learnable Companding Quantization for Accurate Low-bit Neural Networks—0
Linear Context Transform Block—0
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples—0
Critic Loss for Image Classification—0
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference—0
FEATHERS: Federated Architecture and Hyperparameter Search—0
Critical Hyper-Parameters: No Random, No Cry—0
Learned Gradient Compression for Distributed Deep Learning—0
Learned Image resizing with efficient training (LRET) facilitates improved performance of large-scale digital histopathology image classification models—0
Attention-based Natural Language Person Retrieval—0
Handwritten digit and letter recognition using hybrid dwt-dct with knn and svm classifier—0
CRISPnet: Color Rendition ISP Net—0
Defending Against Universal Perturbations With Shared Adversarial Training—0
HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classification—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
Learning a Deep ConvNet for Multi-label Classification with Partial Labels—0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—0
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers—0
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification—0
Learning and Interpreting Multi-Multi-Instance Learning Networks—0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains—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