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 551575 of 10419 papers

TitleStatusHype
Convolutional Channel-wise Competitive Learning for the Forward-Forward AlgorithmCode1
Semantic-Aware Autoregressive Image Modeling for Visual Representation LearningCode1
Transformers in Unsupervised Structure-from-MotionCode1
SA^2VP: Spatially Aligned-and-Adapted Visual PromptCode1
Gradient-based Parameter Selection for Efficient Fine-TuningCode1
Improving Cross-domain Few-shot Classification with Multilayer PerceptronCode1
A Survey of Classical And Quantum Sequence ModelsCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Language-Guided Transformer for Federated Multi-Label ClassificationCode1
Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive NetworksCode1
MaxQ: Multi-Axis Query for N:M Sparsity NetworkCode1
Exploiting Label Skews in Federated Learning with Model ConcatenationCode1
QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick Damaged-building DetectionCode1
Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image ClassificationCode1
Large Language Models are Good Prompt Learners for Low-Shot Image ClassificationCode1
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic SegmentationCode1
Graph Convolutions Enrich the Self-Attention in Transformers!Code1
Foundation Model Assisted Weakly Supervised Semantic SegmentationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model PerspectiveCode1
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level TeacherCode1
BCN: Batch Channel Normalization for Image ClassificationCode1
Automating Continual LearningCode1
Deep Unlearning: Fast and Efficient Gradient-free Approach to Class ForgettingCode1
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image RecognitionCode1
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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