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 5126–5150 of 10420 papers

TitleStatusHype
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends—0
Establishment of Neural Networks Robust to Label Noise—0
Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies—0
Class-relevant Patch Embedding Selection for Few-Shot Image Classification—0
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift—0
Appending Adversarial Frames for Universal Video Attack—0
Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization—0
EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference—0
Equivariant Neural Tangent Kernels—0
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification—0
A Physics-Inspired Optimizer: Velocity Regularized Adam—0
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique—0
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking—0
A^2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems—0
Equivariance with Learned Canonicalization Functions—0
Class Instance Balanced Learning for Long-Tailed Classification—0
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models—0
Class-Incremental Mixture of Gaussians for Deep Continual Learning—0
A Perspective on Deep Vision Performance with Standard Image and Video Codecs—0
Class Incremental Learning with Task-Specific Batch Normalization and Out-of-Distribution Detection—0
A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing—0
Adversarial Dropout Regularization—0
Entropy Induced Pruning Framework for Convolutional Neural Networks—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