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 351400 of 10419 papers

TitleStatusHype
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
Shredder: Learning Noise Distributions to Protect Inference PrivacyCode1
CondenseNet V2: Sparse Feature Reactivation for Deep NetworksCode1
Container: Context Aggregation NetworkCode1
A Comprehensive Survey on Graph Neural NetworksCode1
A Robust Feature Downsampling Module for Remote Sensing Visual TasksCode1
Compressing Features for Learning with Noisy LabelsCode1
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural NetworkCode1
Compositional Explanations of NeuronsCode1
Stateful ODE-Nets using Basis Function ExpansionsCode1
Compressive Visual RepresentationsCode1
Can We Talk Models Into Seeing the World Differently?Code1
Are These Birds Similar: Learning Branched Networks for Fine-grained RepresentationsCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
A Simple Baseline for Low-Budget Active LearningCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
A Survey of Classical And Quantum Sequence ModelsCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
Concept Learners for Few-Shot LearningCode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
Contrastive Learning of Generalized Game RepresentationsCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
A Rainbow in Deep Network Black BoxesCode1
Collaborative Transformers for Grounded Situation RecognitionCode1
Combining Human Predictions with Model Probabilities via Confusion Matrices and CalibrationCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
AQD: Towards Accurate Fully-Quantized Object DetectionCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
A Comprehensive Empirical Evaluation on Online Continual LearningCode1
Arch-Net: Model Distillation for Architecture Agnostic Model DeploymentCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
A Comprehensive Approach to Unsupervised Embedding Learning based on AND AlgorithmCode1
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
Co2L: Contrastive Continual LearningCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
Co^2L: Contrastive Continual LearningCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
ViViT: A Video Vision TransformerCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
A Partially Reversible U-Net for Memory-Efficient Volumetric Image SegmentationCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
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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