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
Divergences in Color Perception between Deep Neural Networks and HumansCode1
Consistency-based Active Learning for Object DetectionCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
Contrastive Deep SupervisionCode1
DivideMix: Learning with Noisy Labels as Semi-supervised LearningCode1
DKDFN: Domain Knowledge-Guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classificationCode1
DMT-JEPA: Discriminative Masked Targets for Joint-Embedding Predictive ArchitectureCode1
DO-Conv: Depthwise Over-parameterized Convolutional LayerCode1
Do Deep Networks Transfer Invariances Across Classes?Code1
Does VLM Classification Benefit from LLM Description Semantics?Code1
A Fully Tensorized Recurrent Neural NetworkCode1
A Simple Baseline for Low-Budget Active LearningCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
Controllable Orthogonalization in Training DNNsCode1
Do Vision Transformers See Like Convolutional Neural Networks?Code1
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse TrainingCode1
Conditional Positional Encodings for Vision TransformersCode1
Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNetCode1
Container: Context Aggregation NetworkCode1
A fuzzy distance-based ensemble of deep models for cervical cancer detectionCode1
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image ClassificationCode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
A Fuzzy Rank-based Ensemble of CNN Models for Classification of Cervical CytologyCode1
Dual-stage Hyperspectral Image Classification Model with Spectral SupertokenCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of EnsemblesCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
CondenseNet V2: Sparse Feature Reactivation for Deep NetworksCode1
Dynamic Group Convolution for Accelerating Convolutional Neural NetworksCode1
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal InformationCode1
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse DataCode1
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersCode1
DynaMixer: A Vision MLP Architecture with Dynamic MixingCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image ClassificationCode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
Differentiable Model Scaling using Differentiable TopkCode1
Contextual Diversity for Active LearningCode1
Age Estimation Using Expectation of Label Distribution LearningCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
EEEA-Net: An Early Exit Evolutionary Neural Architecture SearchCode1
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
Dense Contrastive Learning for Self-Supervised Visual Pre-TrainingCode1
Continual Hippocampus Segmentation with TransformersCode1
Efficient Deep Learning of Non-local Features for Hyperspectral Image ClassificationCode1
Densely Connected Convolutional NetworksCode1
Contextual Transformer Networks for Visual RecognitionCode1
Depth Uncertainty in Neural NetworksCode1
Concept Learners for Few-Shot LearningCode1
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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