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 2626–2650 of 10420 papers

TitleStatusHype
Mixture of Self-Supervised LearningCode0
Understanding Silent Failures in Medical Image ClassificationCode1
Contrastive Knowledge Amalgamation for Unsupervised Image Classification—0
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image ClassificationCode1
ECO: Ensembling Context Optimization for Vision-Language Models—0
MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation—0
Exploring the Sharpened Cosine Similarity—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution NetworksCode0
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification—0
Maximal Independent Sets for Pooling in Graph Neural Networks—0
Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance—0
Concept-based explainability for an EEG transformer modelCode0
Towards a Visual-Language Foundation Model for Computational Pathology—0
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment—0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
Sparse then Prune: Toward Efficient Vision TransformersCode0
GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation ModelsCode1
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?Code1
CLR: Channel-wise Lightweight Reprogramming for Continual LearningCode1
GIST: Generating Image-Specific Text for Fine-grained Object ClassificationCode1
Tuning Pre-trained Model via Moment ProbingCode1
Quantized Feature Distillation for Network Quantization—0
Deep learning for classification of noisy QR codes—0
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model InterpolationCode1
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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