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 18011825 of 10420 papers

TitleStatusHype
Open-sourced Dataset Protection via Backdoor WatermarkingCode1
Shape-Texture Debiased Neural Network TrainingCode1
MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray ModelsCode1
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image ClassificationCode1
Regularizing Neural Networks via Adversarial Model PerturbationCode1
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image ClassificationCode1
A Fully Tensorized Recurrent Neural NetworkCode1
Learning Binary Semantic Embedding for Histology Image Classification and RetrievalCode1
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network InferenceCode1
Rotate to Attend: Convolutional Triplet Attention ModuleCode1
A Data Set and a Convolutional Model for Iconography Classification in PaintingsCode1
Learning with Instance-Dependent Label Noise: A Sample Sieve ApproachCode1
Long-tailed Recognition by Routing Diverse Distribution-Aware ExpertsCode1
Supporting large-scale image recognition with out-of-domain samplesCode1
Bounding Boxes Are All We Need: Street View Image Classification via Context Encoding of Detected BuildingsCode1
Neural BootstrapperCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene UnderstandingCode1
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of EnsemblesCode1
Attentional Feature FusionCode1
Asymmetric Loss For Multi-Label ClassificationCode1
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal EffectCode1
From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic SegmentationCode1
HetSeq: Distributed GPU Training on Heterogeneous InfrastructureCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
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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