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

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Showing 2401–2425 of 10420 papers

TitleStatusHype
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
Are Sample-Efficient NLP Models More Robust?—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
Combining pretrained CNN feature extractors to enhance clustering of complex natural images—0
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
Combining multiple resolutions into hierarchical representations for kernel-based image classification—0
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches—0
Distribution Adaptive INT8 Quantization for Training CNNs—0
Robust Multi-instance Learning with Stable Instances—0
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions—0
Adversarial Transformations for Semi-Supervised Learning—0
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification—0
Combining Deep Learning with Good Old-Fashioned Machine Learning—0
A Relational Model for One-Shot Classification—0
A Contrastive Learning Approach to Auroral Identification and Classification—0
Combinets: Creativity via Recombination of Neural Networks—0
Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
Combined statistical and model based texture features for improved image classification—0
Combined Scaling for Zero-shot Transfer Learning—0
Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?—0
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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