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 351375 of 10419 papers

TitleStatusHype
Co^2L: Contrastive Continual LearningCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
AQD: Towards Accurate Fully-Quantized Object DetectionCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
A Rainbow in Deep Network Black BoxesCode1
Shredder: Learning Noise Distributions to Protect Inference PrivacyCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
A Comprehensive Survey on Graph Neural NetworksCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
FocusNet: Classifying Better by Focusing on Confusing ClassesCode1
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
A Partially Reversible U-Net for Memory-Efficient Volumetric Image SegmentationCode1
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network InferenceCode1
Anytime Continual Learning for Open Vocabulary ClassificationCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object NavigationCode1
CLR: Channel-wise Lightweight Reprogramming for Continual LearningCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
A Novel lightweight Convolutional Neural Network, ExquisiteNetV2Code1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
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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