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 551575 of 10419 papers

TitleStatusHype
Co^2L: Contrastive Continual LearningCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
Co2L: Contrastive Continual LearningCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
CLR: Channel-wise Lightweight Reprogramming for Continual LearningCode1
AdaViT: Adaptive Tokens for Efficient Vision TransformerCode1
FocusNet: Classifying Better by Focusing on Confusing ClassesCode1
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image propertiesCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
A Data Set and a Convolutional Model for Iconography Classification in PaintingsCode1
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object NavigationCode1
AdaScale SGD: A User-Friendly Algorithm for Distributed TrainingCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image CollectionsCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
CLIP4IDC: CLIP for Image Difference CaptioningCode1
CLCC: Contrastive Learning for Color ConstancyCode1
CLCNet: Rethinking of Ensemble Modeling with Classification Confidence NetworkCode1
Class-Difficulty Based Methods for Long-Tailed Visual RecognitionCode1
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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