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 651675 of 10419 papers

TitleStatusHype
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image ClassificationCode1
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image ClassificationCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Adaptive Edge Offloading for Image Classification Under Rate LimitCode1
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural NetworkCode1
Stateful ODE-Nets using Basis Function ExpansionsCode1
An Empirical Investigation of the Role of Pre-training in Lifelong LearningCode1
An Empirical Investigation of Representation Learning for ImitationCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
CondenseNet V2: Sparse Feature Reactivation for Deep NetworksCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Conformer: Local Features Coupling Global Representations for Visual RecognitionCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Container: Context Aggregation NetworkCode1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
Contextual Transformer Networks for Visual RecognitionCode1
DCN-T: Dual Context Network with Transformer for Hyperspectral Image ClassificationCode1
Continual Hippocampus Segmentation with TransformersCode1
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikesCode1
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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