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 10001–10050 of 10420 papers

TitleStatusHype
KerCNNs: biologically inspired lateral connections for classification of corrupted images—0
Kernel Descriptors for Visual Recognition—0
Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification—0
Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition—0
Kernelized Support Tensor Train Machines—0
Kernel Methods in Hyperbolic Spaces—0
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification—0
Kernel principal component analysis network for image classification—0
Kernel Reconstruction ICA for Sparse Representation—0
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification—0
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis—0
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics—0
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning—0
Knee or ROC—0
Knowledge accumulating: The general pattern of learning—0
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models—0
Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN—0
Knowledge Distillation for Incremental Learning in Semantic Segmentation—0
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation—0
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students—0
Knowledge Distillation in Vision Transformers: A Critical Review—0
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains—0
Knowledge distillation using unlabeled mismatched images—0
Knowledge Distillation with Feature Maps for Image Classification—0
Representative Teacher Keys for Knowledge Distillation Model Compression Based on Attention Mechanism for Image Classification—0
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition—0
Knowledge-Free Black-Box Watermark and Ownership Proof for Image Classification Neural Networks—0
EA-KD: Entropy-based Adaptive Knowledge Distillation—0
Categorical Knowledge Fused Recognition: Fusing Hierarchical Knowledge with Image Classification through Aligning and Deep Metric Learning—0
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports—0
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation—0
KO: Kinetics-inspired Neural Optimizer with PDE Simulation Approaches—0
Kolmogorov-Arnold networks for metal surface defect classification—0
KTAN: Knowledge Transfer Adversarial Network—0
Label Consistent Transform Learning for Hyperspectral Image Classification—0
Label Denoising through Cross-Model Agreement—0
Label-Efficient Group Robustness via Out-of-Distribution Concept Curation—0
Label-efficient Single Photon Images Classification via Active Learning—0
Label-Embedding for Attribute-Based Classification—0
Label Embedding with Partial Heterogeneous Contexts—0
Label Geometry Aware Discriminator for Conditional Generative Networks—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
Label-similarity Curriculum Learning—0
Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification—0
LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization—0
Laconic Deep Learning Computing—0
Laconic Image Classification: Human vs. Machine Performance—0
LaCoOT: Layer Collapse through Optimal Transport—0
Ladder Networks for Semi-Supervised Hyperspectral Image Classification—0
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units—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