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 2101–2150 of 10419 papers

TitleStatusHype
Text as Image: Learning Transferable Adapter for Multi-Label Classification—0
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks—0
Foundation Model Assisted Weakly Supervised Semantic SegmentationCode1
Riemannian Complex Matrix Convolution Network for PolSAR Image Classification—0
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry—0
GDN: A Stacking Network Used for Skin Cancer DiagnosisCode0
Classification for everyone : Building geography agnostic models for fairer recognition—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
Federated Active Learning for Target Domain GeneralisationCode0
A Comprehensive Literature Review on Sweet Orange Leaf Diseases—0
MABViT -- Modified Attention Block Enhances Vision Transformers—0
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model PerspectiveCode1
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation—0
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer—0
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level TeacherCode1
Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines—0
A Comprehensive Study of Vision Transformers in Image Classification Tasks—0
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment AnythingCode4
Physics Inspired Criterion for Pruning-Quantization Joint LearningCode0
SCHEME: Scalable Channel Mixer for Vision Transformers—0
Improving Normalization with the James-Stein Estimator—0
PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight PredictionCode0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Developmental Pretraining (DPT) for Image Classification NetworksCode0
Automating Continual LearningCode1
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—0
Deep Unlearning: Fast and Efficient Gradient-free Approach to Class ForgettingCode1
BCN: Batch Channel Normalization for Image ClassificationCode1
TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion ModelsCode2
CLIP-QDA: An Explainable Concept Bottleneck Model—0
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image RecognitionCode1
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
Negotiated Representations to Prevent Forgetting in Machine Learning ApplicationsCode0
Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications—0
Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation—0
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters—0
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans—0
Rethinking Image Editing Detection in the Era of Generative AI Revolution—0
Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels—0
LayerCollapse: Adaptive compression of neural networks—0
Meta Co-Training: Two Views are Better than OneCode1
Enhancing Post-Hoc Explanation Benchmark Reliability for Image Classification—0
Do text-free diffusion models learn discriminative visual representations?Code1
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Automatic Recognition of Learning Resource Category in a Digital LibraryCode0
PHG-Net: Persistent Homology Guided Medical Image ClassificationCode1
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model—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
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified