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 2151–2200 of 10419 papers

TitleStatusHype
Simplifying Graph Convolutional NetworksCode1
Evolutionary Neural AutoML for Deep LearningCode1
Learning From Noisy Labels By Regularized Estimation Of Annotator ConfusionCode1
Parameter-Efficient Transfer Learning for NLPCode1
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift AdaptationCode1
Class-Balanced Loss Based on Effective Number of SamplesCode1
A Comprehensive Survey on Graph Neural NetworksCode1
On Minimum Discrepancy Estimation for Deep Domain AdaptationCode1
Studying the Plasticity in Deep Convolutional Neural Networks using Random PruningCode1
Semi-Supervised Deep Learning for Abnormality Classification in Retinal ImagesCode1
Proximal Mean-field for Neural Network QuantizationCode1
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture SearchCode1
Few-shot Object Detection via Feature ReweightingCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation LearningCode1
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnessCode1
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks AccelerationCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Shallow-Deep Networks: Understanding and Mitigating Network OverthinkingCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
SNIP: Single-shot Network Pruning based on Connection SensitivityCode1
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural NetworksCode1
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised DetectionCode1
A Less Biased Evaluation of Out-of-distribution Sample DetectorsCode1
Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' BlocksCode1
Automatically designing CNN architectures using genetic algorithm for image classificationCode1
MnasNet: Platform-Aware Neural Architecture Search for MobileCode1
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture DesignCode1
End-to-End Incremental LearningCode1
Invariant Information Clustering for Unsupervised Image Classification and SegmentationCode1
Age Estimation Using Expectation of Label Distribution LearningCode1
This Looks Like That: Deep Learning for Interpretable Image RecognitionCode1
DARTS: Differentiable Architecture SearchCode1
RISE: Randomized Input Sampling for Explanation of Black-box ModelsCode1
Manifold Mixup: Better Representations by Interpolating Hidden StatesCode1
Bayesian Model-Agnostic Meta-LearningCode1
Improving the Resolution of CNN Feature Maps Efficiently with MultisamplingCode1
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy LabelsCode1
Wavelet Convolutional Neural NetworksCode1
Robust Classification with Convolutional Prototype LearningCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentationCode1
BAGAN: Data Augmentation with Balancing GANCode1
Averaging Weights Leads to Wider Optima and Better GeneralizationCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
Directional Statistics-based Deep Metric Learning for Image Classification and RetrievalCode1
Encoder-Decoder with Atrous Separable Convolution for Semantic Image SegmentationCode1
MobileNetV2: Inverted Residuals and Linear BottlenecksCode1
Maximum Classifier Discrepancy for Unsupervised Domain AdaptationCode1
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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