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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2851–2875 of 10420 papers

TitleStatusHype
Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification—0
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled DataCode0
Exploring Robustness of Image Recognition Models on Hardware AcceleratorsCode0
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-TuningCode1
Vocabulary-free Image ClassificationCode1
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One DayCode4
A Robust Feature Downsampling Module for Remote Sensing Visual TasksCode1
Pseudo Labels for Single Positive Multi-Label Learning—0
Addressing Discrepancies in Semantic and Visual Alignment in Neural Networks—0
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification—0
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity OptimizationCode1
Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach—0
GPT4Image: Can Large Pre-trained Models Help Vision Models on Perception Tasks?—0
Doubly Robust Self-TrainingCode0
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training QuantizationCode0
On the Limitations of Temperature Scaling for Distributions with OverlapsCode0
Training-free Neural Architecture Search for RNNs and TransformersCode1
Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shiftCode0
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning—0
Hiera: A Hierarchical Vision Transformer without the Bells-and-WhistlesCode0
Microstructure quality control of steels using deep learning—0
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy LabelsCode1
Bytes Are All You Need: Transformers Operating Directly On File Bytes—0
Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNCode1
Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified