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

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Showing 6301–6350 of 10420 papers

TitleStatusHype
MLP-based architecture with variable length input for automatic speech recognition—0
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications—0
Learning to Schedule Learning rate with Graph Neural Networks—0
Multi-loss ensemble deep learning for chest X-ray classification—0
Towards Efficient On-Chip Training of Quantum Neural Networks—0
Tessellated 2D Convolution Networks: A Robust Defence against Adversarial Attacks—0
Best Practices in Pool-based Active Learning for Image Classification—0
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces—0
Noise-Contrastive Variational Information Bottleneck Networks—0
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation—0
Adaptive Region Pooling for Fine-Grained Representation Learning—0
Ontology-Driven Semantic Alignment of Artificial Neurons and Visual Concepts—0
On the Convergence of Nonconvex Continual Learning with Adaptive Learning Rate—0
Task Conditioned Stochastic Subsampling—0
Self-supervised Models are Good Teaching Assistants for Vision Transformers—0
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods—0
Representation Disentanglement in Generative Models with Contrastive Learning—0
UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION—0
UFO-ViT: High Performance Linear Vision Transformer without SoftmaxCode0
Invariance-Guided Feature Evolution for Few-Shot Learning—0
Interventional Black-Box Explanations—0
Sample-specific and Context-aware Augmentation for Long Tail Image Classification—0
Sparse Attention with Learning to Hash—0
Informative Robust Causal Representation for Generalizable Deep Learning—0
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models—0
Monotonicity as a requirement and as a regularizer: efficient methods and applications—0
Improvising the Learning of Neural Networks on Hyperspherical ManifoldCode0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
Mistake-driven Image Classification with FastGAN and SpinalNet—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Image BERT Pre-training with Online Tokenizer—0
iFlood: A Stable and Effective Regularizer—0
How to Adapt Your Large-Scale Vision-and-Language Model—0
When in Doubt, Summon the Titans: A Framework for Efficient Inference with Large Models—0
HFSP: A Hardware-friendly Soft Pruning Framework for Vision Transformers—0
Hermitry Ratio: Evaluating the validity of perturbation methods for explainable deep learning—0
Cross Domain Ensemble Distillation for Domain Generalization—0
Rethinking Client Reweighting for Selfish Federated Learning—0
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—0
Rank4Class: Examining Multiclass Classification through the Lens of Learning to Rank—0
Regularizing Image Classification Neural Networks with Partial Differential Equations—0
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition—0
Contrastively Enforcing Distinctiveness for Multi-Label Classification—0
Towards Generic Interface for Human-Neural Network Knowledge Exchange—0
Fundamental Limits of Transfer Learning in Binary Classifications—0
Function-Space Variational Inference for Deep Bayesian Classification—0
Assessing two novel distance-based loss functions for few-shot image classification—0
FLOAT: FAST LEARNABLE ONCE-FOR-ALL ADVERSARIAL TRAINING FOR TUNABLE TRADE-OFF BETWEEN ACCURACY AND ROBUSTNESS—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
A Contrastive Learning Approach to Auroral Identification and Classification—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