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 5776–5800 of 10420 papers

TitleStatusHype
Distant Domain Transfer Learning for Medical Imaging—0
Distangling Biological Noise in Cellular Images with a focus on Explainability—0
Adaptive Gradient Clipping for Robust Federated Learning—0
Distance-based Composable Representations with Neural Networks—0
Dissonance Between Human and Machine Understanding—0
Boosting Network Weight Separability via Feed-Backward Reconstruction—0
An empirical study of the relation between network architecture and complexity—0
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data—0
Disrupting Model Training with Adversarial Shortcuts—0
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy—0
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning—0
Boosting Medical Image Classification with Segmentation Foundation Model—0
Disentangling Visual Transformers: Patch-level Interpretability for Image Classification—0
Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning—0
Disentangling CLIP for Multi-Object Perception—0
Boosting Hyperspectral Image Classification with Gate-Shift-Fuse Mechanisms in a Novel CNN-Transformer Approach—0
An empirical study of pretrained representations for few-shot classification—0
Boosting Gradient for White-Box Adversarial Attacks—0
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization—0
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination—0
Boosting for Bounding the Worst-class Error—0
An Empirical Study of Adder Neural Networks for Object Detection—0
Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models—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