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 11511175 of 10420 papers

TitleStatusHype
ActMAD: Activation Matching to Align Distributions for Test-Time-TrainingCode1
Attentional-Biased Stochastic Gradient DescentCode1
Attentional Feature FusionCode1
BitQ: Tailoring Block Floating Point Precision for Improved DNN Efficiency on Resource-Constrained DevicesCode1
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image ClassificationCode1
Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image ClassificationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Black-box Few-shot Knowledge DistillationCode1
Attention-Based Second-Order Pooling Network for Hyperspectral Image ClassificationCode1
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural NetworksCode1
Conditional Positional Encodings for Vision TransformersCode1
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box AttacksCode1
Benchmarking Test-Time Adaptation against Distribution Shifts in Image ClassificationCode1
"BNN - BN = ?": Training Binary Neural Networks without Batch NormalizationCode1
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagationCode1
AIDeveloper: deep learning image classification in life science and beyondCode1
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecksCode1
Boosting Convolutional Neural Networks with Middle Spectrum Grouped ConvolutionCode1
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data AugmentationCode1
Asymmetric Polynomial Loss For Multi-Label ClassificationCode1
A Call to Reflect on Evaluation Practices for Failure Detection in Image ClassificationCode1
Boosting Multi-Label Image Classification with Complementary Parallel Self-DistillationCode1
Boosting the Adversarial Transferability of Surrogate Models with Dark KnowledgeCode1
Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNetCode1
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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