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 801–850 of 10419 papers

TitleStatusHype
Dynamic Token Pruning in Plain Vision Transformers for Semantic SegmentationCode1
DynamicViT: Efficient Vision Transformers with Dynamic Token SparsificationCode1
Compressive Visual RepresentationsCode1
Contextual Convolutional Neural NetworksCode1
CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare RecordsCode1
EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled RegularizationCode1
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
TransCenter: Transformers with Dense Representations for Multiple-Object TrackingCode1
Disentangling Label Distribution for Long-tailed Visual RecognitionCode1
EEEA-Net: An Early Exit Evolutionary Neural Architecture SearchCode1
Efficiency 360: Efficient Vision TransformersCode1
Efficient Adaptation of Large Vision Transformer via Adapter Re-ComposingCode1
Can An Image Classifier Suffice For Action Recognition?Code1
Efficient Classification of Very Large Images with Tiny ObjectsCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
Co2L: Contrastive Continual LearningCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz NetworksCode1
Co^2L: Contrastive Continual LearningCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
All you need is a good initCode1
A Dual-Direction Attention Mixed Feature Network for Facial Expression RecognitionCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
EfficientPose: Efficient Human Pose Estimation with Neural Architecture SearchCode1
A Single Graph Convolution Is All You Need: Efficient Grayscale Image ClassificationCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
Adaptive and Background-Aware Vision Transformer for Real-Time UAV TrackingCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
Emerging Properties in Self-Supervised Vision TransformersCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent BackpropagationCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
End-to-End Incremental LearningCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal RepresentationsCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Enhancing Few-shot Image Classification with Cosine TransformerCode1
Ensembling with Deep Generative ViewsCode1
EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image ClassificationCode1
FocusNet: Classifying Better by Focusing on Confusing ClassesCode1
ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill IdentificationCode1
Advancing Vision Transformers with Group-Mix AttentionCode1
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional NetworkCode1
Advantages and Bottlenecks of Quantum Machine Learning for Remote SensingCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
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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