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 10076–10100 of 10420 papers

TitleStatusHype
Lateralized Learning for Multi-Class Visual Classification Tasks—0
LayerCollapse: Adaptive compression of neural networks—0
Layer-Specific Adaptive Learning Rates for Deep Networks—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
A Generic Shared Attention Mechanism for Various Backbone Neural Networks—0
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding—0
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems—0
L-CNN: A Lattice cross-fusion strategy for multistream convolutional neural networks—0
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition—0
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding—0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise—0
LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation—0
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks—0
LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks—0
LEAP: Learning Embeddings for Adaptive Pace—0
Learnable Bernoulli Dropout for Bayesian Deep Learning—0
Learnable Companding Quantization for Accurate Low-bit Neural Networks—0
Learnable Pooling Regions for Image Classification—0
Learned Gradient Compression for Distributed Deep Learning—0
Learned Image resizing with efficient training (LRET) facilitates improved performance of large-scale digital histopathology image classification models—0
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning—0
Learning a Deep ConvNet for Multi-label Classification with Partial Labels—0
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified