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 28762900 of 10420 papers

TitleStatusHype
Problem-dependent convergence bounds for randomized linear gradient compression0
Invariant Shape Representation Learning For Image ClassificationCode0
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification0
Exploring Emerging Trends and Research Opportunities in Visual Place Recognition0
Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging0
Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning0
Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image0
Deep Feature Response Discriminative CalibrationCode0
Multi-perspective Contrastive Logit Distillation0
Hysteresis Activation Function for Efficient InferenceCode0
Evidential Federated Learning for Skin Lesion Image Classification0
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring PluginCode0
Adapting the Biological SSVEP Response to Artificial Neural Networks0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation0
Outliers resistant image classification by anomaly detection0
ResidualDroppath: Enhancing Feature Reuse over Residual Connections0
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering0
SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision TransformersCode0
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
Efficient Whole Slide Image Classification through Fisher Vector Representation0
ScaleNet: Scale Invariance Learning in Directed GraphsCode0
Computed tomography using meta-optics0
Semantic segmentation on multi-resolution optical and microwave data using deep learning0
Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision0
Deep Active Learning in the Open World0
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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