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

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Showing 5051–5100 of 10420 papers

TitleStatusHype
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded SystemsCode0
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning—0
Focal Attention for Long-Range Interactions in Vision TransformersCode1
argmax centroid—0
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble ModelsCode0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
Explanation-based Data Augmentation for Image ClassificationCode0
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond KernelsCode0
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks—0
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation LearningCode1
Bayesian Adaptation for Covariate Shift—0
Memory-efficient Patch-based Inference for Tiny Deep Learning—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
Pooling by Sliced-Wasserstein EmbeddingCode1
I-CNet: Leveraging Involution and Convolution for Image Classification—0
Sound-Guided Semantic Image ManipulationCode1
Pyramid Adversarial Training Improves ViT PerformanceCode0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning—0
Human Imperceptible Attacks and Applications to Improve Fairness—0
The Devil is in the Margin: Margin-based Label Smoothing for Network CalibrationCode1
Adaptive Token Sampling For Efficient Vision TransformersCode1
MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at ScaleCode1
Weakly-supervised Generative Adversarial Networks for medical image classification—0
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data—0
Building extraction with vision transformer—0
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable PrototypesCode1
On the Effectiveness of Neural Ensembles for Image Classification with Small Datasets—0
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images—0
Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images—0
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox—0
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification—0
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual RecognitionCode1
Reinforcement Explanation Learning—0
KNAS: Green Neural Architecture SearchCode1
ExPLoit: Extracting Private Labels in Split Learning—0
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
FedDropoutAvg: Generalizable federated learning for histopathology image classification—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
ML-Decoder: Scalable and Versatile Classification HeadCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Global Interaction Modelling in Vision Transformer via Super Tokens—0
Application of deep learning to camera trap data for ecologists in planning / engineering -- Can captivity imagery train a model which generalises to the wild?—0
Transferability Estimation using Bhattacharyya Class Separability—0
Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates—0
Sharpness-aware Quantization for Deep Neural NetworksCode1
PeCo: Perceptual Codebook for BERT Pre-training of Vision TransformersCode1
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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
10RevCol-HTop 1 Accuracy90—Unverified