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 27762800 of 10420 papers

TitleStatusHype
DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image ClassificationCode0
Predicting the Reliability of an Image Classifier under Image Distortion0
Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image ClassificationCode0
Adversarial Attack Against Images Classification based on Generative Adversarial Networks0
Revisiting MLLMs: An In-Depth Analysis of Image Classification Abilities0
UNEM: UNrolled Generalized EM for Transductive Few-Shot LearningCode0
Sensitive Image Classification by Vision Transformers0
PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation0
V"Mean"ba: Visual State Space Models only need 1 hidden dimension0
FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification0
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot LearningCode0
Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization LayersCode0
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labelsCode0
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection0
MBInception: A new Multi-Block Inception Model for Enhancing Image Processing Efficiency0
Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models0
RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image ClassificationCode0
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K SamplingCode0
ShotVL: Human-Centric Highlight Frame Retrieval via Language Queries0
Structural Pruning via Spatial-aware Information Redundancy for Semantic SegmentationCode0
Identifying Bias in Deep Neural Networks Using Image TransformsCode0
CNNtention: Can CNNs do better with Attention?Code0
The Impact of Generalization Techniques on the Interplay Among Privacy, Utility, and Fairness in Image Classification0
Real-valued continued fraction of straight linesCode0
Explicit and Implicit Graduated Optimization in Deep Neural NetworksCode0
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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