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 401425 of 10419 papers

TitleStatusHype
Combating Label Noise in Deep Learning Using AbstentionCode1
Compressive Visual RepresentationsCode1
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object NavigationCode1
A Dual-Direction Attention Mixed Feature Network for Facial Expression RecognitionCode1
CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image CollectionsCode1
CLIP4IDC: CLIP for Image Difference CaptioningCode1
3D U^2-Net: A 3D Universal U-Net for Multi-Domain Medical Image SegmentationCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
Class-Incremental Grouping Network for Continual Audio-Visual LearningCode1
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image EncodingCode1
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikesCode1
CLCC: Contrastive Learning for Color ConstancyCode1
4-bit Shampoo for Memory-Efficient Network TrainingCode1
A fuzzy distance-based ensemble of deep models for cervical cancer detectionCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Class-Difficulty Based Methods for Long-Tailed Visual RecognitionCode1
ViViT: A Video Vision TransformerCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
TransCenter: Transformers with Dense Representations for Multiple-Object TrackingCode1
Advancing Vision Transformers with Group-Mix AttentionCode1
Advantages and Bottlenecks of Quantum Machine Learning for Remote SensingCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity EstimationCode1
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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