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 11011150 of 10419 papers

TitleStatusHype
Curriculum By SmoothingCode1
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative LearningCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale AttentionCode1
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN ExecutionCode1
Co-Tuning for Transfer LearningCode1
Counterfactual Visual ExplanationsCode1
CrAM: A Compression-Aware MinimizerCode1
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised LearningCode1
CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision ModelsCode1
Convolutional Spiking Neural Networks for Spatio-Temporal Feature ExtractionCode1
CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare RecordsCode1
ConvMLP: Hierarchical Convolutional MLPs for VisionCode1
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy LabelsCode1
Convolutional Channel-wise Competitive Learning for the Forward-Forward AlgorithmCode1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
A graph-transformer for whole slide image classificationCode1
Contrastive Learning of Generalized Game RepresentationsCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
Contrastive Deep SupervisionCode1
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecksCode1
Asymmetric Polynomial Loss For Multi-Label ClassificationCode1
A synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classificationCode1
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic SegmentationCode1
Augmentation Strategies for Learning with Noisy LabelsCode1
Controllable Orthogonalization in Training DNNsCode1
A Call to Reflect on Evaluation Practices for Failure Detection in Image ClassificationCode1
Active Token MixerCode1
Convolutional Sequence to Sequence LearningCode1
Convolutional Xformers for VisionCode1
Convolution-enhanced Evolving Attention NetworksCode1
Contrastive Learning Improves Model Robustness Under Label NoiseCode1
Counterfactual Generative NetworksCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 DiagnosisCode1
Augmenting Convolutional networks with attention-based aggregationCode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Augmented Neural ODEsCode1
Augmented Neural Fine-Tuning for Efficient Backdoor PurificationCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
Adaptive Token Sampling For Efficient Vision TransformersCode1
Cross-Layer Retrospective Retrieving via Layer AttentionCode1
Cross-modal Adversarial ReprogrammingCode1
Curriculum Temperature for Knowledge DistillationCode1
Benchmarking Pathology Feature Extractors for Whole Slide Image ClassificationCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
Asymmetric Loss For Multi-Label ClassificationCode1
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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