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 6651–6700 of 10420 papers

TitleStatusHype
Benchmarking Inference Performance of Deep Learning Models on Analog Devices—0
Multiple EffNet/ResNet Architectures for Melanoma Classification—0
Learnable Pooling Regions for Image Classification—0
Defending Adversaries Using Unsupervised Feature Clustering VAE—0
Benchmarking FedAvg and FedCurv for Image Classification Tasks—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories—0
Analysis Dictionary Learning: An Efficient and Discriminative Solution—0
Adaptive sampling for scanning pixel cameras—0
Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification—0
ACIL: Active Class Incremental Learning for Image Classification—0
SNR and Resource Adaptive Deep JSCC for Distributed IoT Image Classification—0
Tag-based Semantic Features for Scene Image Classification—0
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1—0
Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning—0
What Do Single-view 3D Reconstruction Networks Learn?—0
Learnable Companding Quantization for Accurate Low-bit Neural Networks—0
Learnable Bernoulli Dropout for Bayesian Deep Learning—0
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant—0
DeepVO: A Deep Learning approach for Monocular Visual Odometry—0
LEAP: Learning Embeddings for Adaptive Pace—0
LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks—0
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks—0
Deep Visual Domain Adaptation: A Survey—0
Deep Visual Domain Adaptation—0
LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation—0
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise—0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding—0
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition—0
Multi-Scale Prototypical Transformer for Whole Slide Image Classification—0
Multi-scale recognition with DAG-CNNs—0
L-CNN: A Lattice cross-fusion strategy for multistream convolutional neural networks—0
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model—0
Benchmarking Adversarial Robustness—0
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification—0
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems—0
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding—0
Benchmarking a Benchmark: How Reliable is MS-COCO?—0
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding—0
Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification—0
A Generic Shared Attention Mechanism for Various Backbone Neural Networks—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
Layer-Specific Adaptive Learning Rates for Deep Networks—0
Deep Transfer Learning: Model Framework and Error Analysis—0
An Alternative Practice of Tropical Convolution to Traditional Convolutional Neural Networks—0
Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images—0
LayerCollapse: Adaptive compression of neural networks—0
Benchmark data to study the influence of pre-training on explanation performance in MR image 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