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 6401–6425 of 10420 papers

TitleStatusHype
Automated Robustness with Adversarial Training as a Post-Processing Step—0
Cross-token Modeling with Conditional Computation—0
Real-World Adversarial Examples involving Makeup Application—0
ISyNet: Convolutional Neural Networks design for AI acceleratorCode0
CAM-loss: Towards Learning Spatially Discriminative Feature Representations—0
Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models—0
Ghost Loss to Question the Reliability of Training Data—0
Better Self-training for Image Classification through Self-supervisionCode0
Impact of Attention on Adversarial Robustness of Image Classification Models—0
Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study—0
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Multilingual Image Corpus: Annotation Protocol—0
The Impact of Reinitialization on Generalization in Convolutional Neural Networks—0
EG-Booster: Explanation-Guided Booster of ML Evasion AttacksCode0
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations—0
Deep Generative Modeling for Protein Design—0
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs—0
AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks—0
Spike time displacement based error backpropagation in convolutional spiking neural networks—0
Communication-Computation Efficient Device-Edge Co-Inference via AutoML—0
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification—0
Privacy-preserving Machine Learning for Medical Image Classification—0
New Pruning Method Based on DenseNet Network for Image Classification—0
Representation Memorization for Fast Learning New Knowledge without Forgetting—0
Goal-driven text descriptions for images—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified