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

Showing 15511575 of 10420 papers

TitleStatusHype
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic SpaceCode1
MLP-Mixer: An all-MLP Architecture for VisionCode1
LFI-CAM: Learning Feature Importance for Better Visual ExplanationCode1
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated LearningCode1
Adversarial Example Detection for DNN Models: A Review and Experimental ComparisonCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
GeoWINE: Geolocation based Wiki, Image,News and Event RetrievalCode1
Ensembling with Deep Generative ViewsCode1
Emerging Properties in Self-Supervised Vision TransformersCode1
Decoupled Dynamic Filter NetworksCode1
Open-vocabulary Object Detection via Vision and Language Knowledge DistillationCode1
Twins: Revisiting the Design of Spatial Attention in Vision TransformersCode1
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support SamplesCode1
Boosting Co-teaching with Compression Regularization for Label NoiseCode1
EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature FusionCode1
Rethinking BiSeNet For Real-time Semantic SegmentationCode1
Explaining in Style: Training a GAN to explain a classifier in StyleSpaceCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
Wise-SrNet: A Novel Architecture for Enhancing Image Classification by Learning Spatial Resolution of Feature MapsCode1
Mutual Contrastive Learning for Visual Representation LearningCode1
Visformer: The Vision-friendly TransformerCode1
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification DatasetsCode1
Vision Transformers with Patch DiversificationCode1
Carrying out CNN Channel Pruning in a White BoxCode1
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use caseCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified