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 851900 of 10419 papers

TitleStatusHype
Class-Incremental Grouping Network for Continual Audio-Visual LearningCode1
FedMLP: Federated Multi-Label Medical Image Classification under Task HeterogeneityCode1
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive LearningCode1
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikesCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image CollectionsCode1
CLCNet: Rethinking of Ensemble Modeling with Classification Confidence NetworkCode1
CLCC: Contrastive Learning for Color ConstancyCode1
A Comprehensive Approach to Unsupervised Embedding Learning based on AND AlgorithmCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor ClassificationCode1
CLIP4IDC: CLIP for Image Difference CaptioningCode1
Adaptive and Background-Aware Vision Transformer for Real-Time UAV TrackingCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
Depth Uncertainty in Neural NetworksCode1
DetCo: Unsupervised Contrastive Learning for Object DetectionCode1
A Comprehensive Empirical Evaluation on Online Continual LearningCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
FocusNet: Classifying Better by Focusing on Confusing ClassesCode1
Automatically designing CNN architectures using genetic algorithm for image classificationCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
A Novel lightweight Convolutional Neural Network, ExquisiteNetV2Code1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
Fine-Grained Predicates Learning for Scene Graph GenerationCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Firefly Neural Architecture Descent: a General Approach for Growing Neural NetworksCode1
Co^2L: Contrastive Continual LearningCode1
Co2L: Contrastive Continual LearningCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Dendritic Learning-incorporated Vision Transformer for Image RecognitionCode1
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision TasksCode1
Delving into Out-of-Distribution Detection with Medical Vision-Language ModelsCode1
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural NetworksCode1
DenoiseRep: Denoising Model for Representation LearningCode1
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functionsCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
Adversarial AutoMixupCode1
Compositional Explanations of NeuronsCode1
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image ClassificationCode1
DeiT III: Revenge of the ViTCode1
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified