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 41764200 of 10420 papers

TitleStatusHype
On the Subspace Structure of Gradient-Based Meta-Learning0
An Embedding-Dynamic Approach to Self-supervised Learning0
Calibrate to InterpretCode0
DLME: Deep Local-flatness Manifold EmbeddingCode1
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions0
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation0
L_2BN: Enhancing Batch Normalization by Equalizing the L_2 Norms of Features0
Generalization to translation shifts: a study in architectures and augmentations0
CNN-based Local Vision Transformer for COVID-19 Diagnosis0
Predicting Out-of-Domain Generalization with Neighborhood Invariance0
ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image ClassificationCode1
Understanding and Improving Group NormalizationCode0
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning0
Vision-and-Language PretrainingCode0
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch0
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation ModelsCode0
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN ExecutionCode1
FlowNAS: Neural Architecture Search for Optical Flow EstimationCode1
Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer0
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Portuguese Man-of-War Image Classification with Convolutional Neural Networks0
Saliency-Regularized Deep Multi-Task LearningCode1
Can Language Understand Depth?Code1
NP-Match: When Neural Processes meet Semi-Supervised LearningCode1
DecisioNet: A Binary-Tree Structured Neural NetworkCode0
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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