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 21512175 of 10420 papers

TitleStatusHype
Wasserstein Adversarial Examples via Projected Sinkhorn IterationsCode1
Meta-Weight-Net: Learning an Explicit Mapping For Sample WeightingCode1
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
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
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
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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