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 826–850 of 10419 papers

TitleStatusHype
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