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

Showing 54265450 of 10420 papers

TitleStatusHype
Cross-Hierarchical Bidirectional Consistency Learning for Fine-Grained Visual Classification0
AI of Brain and Cognitive Sciences: From the Perspective of First Principles0
Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization0
Deep Multi-Task Learning for Malware Image Classification0
Joint Hierarchical Category Structure Learning and Large-Scale Image Classification0
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes0
Joint Learning of Discriminative Low-dimensional Image Representations Based on Dictionary Learning and Two-layer Orthogonal Projections0
Deep Multi-view Models for Glitch Classification0
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition0
Learning Representations of Graph Data -- A Survey0
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine0
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis0
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints0
Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification0
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods0
Heterogeneous Generative Knowledge Distillation with Masked Image Modeling0
Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification0
Heterogeneous Federated Learning Using Knowledge Codistillation0
Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation0
Cross-filter compression for CNN inference acceleration0
Just rotate it! Uncertainty estimation in closed-source models via multiple queries0
Hermitry Ratio: Evaluating the validity of perturbation methods for explainable deep learning0
Cross-Domain Sparse Coding0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation0
Learning rich optical embeddings for privacy-preserving lensless image classification0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified