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 10101–10150 of 10420 papers

TitleStatusHype
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting—0
Embedding Label Structures for Fine-Grained Feature Representation—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Explaining NonLinear Classification Decisions with Deep Taylor DecompositionCode0
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition—0
Learning The Structure of Deep Convolutional Networks—0
Aggregating Local Deep Features for Image Retrieval—0
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks—0
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking—0
Task-Driven Feature Pooling for Image Classification—0
Structured Feature Selection—0
Design of Kernels in Convolutional Neural Networks for Image ClassificationCode0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks—0
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification—0
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)Code0
Recombinator Networks: Learning Coarse-to-Fine Feature AggregationCode0
Auxiliary Image Regularization for Deep CNNs with Noisy Labels—0
Data-dependent Initializations of Convolutional Neural NetworksCode0
Top-k Multiclass SVMCode0
Training CNNs with Low-Rank Filters for Efficient Image Classification—0
Semantic Diversity versus Visual Diversity in Visual Dictionaries—0
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial NetworksCode0
Geodesics of learned representations—0
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?—0
Compact Bilinear PoolingCode0
Convolutional Clustering for Unsupervised Learning—0
Learning to Generate Images with Perceptual Similarity Metrics—0
Competitive Multi-scale Convolution—0
Sparse learning of maximum likelihood model for optimization of complex loss function—0
Staleness-aware Async-SGD for Distributed Deep LearningCode0
Learning Structured Inference Neural Networks with Label Relations—0
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning—0
Learning Fine-grained Features via a CNN Tree for Large-scale Classification—0
DeepFool: a simple and accurate method to fool deep neural networksCode0
Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning—0
Universum Prescription: Regularization using Unlabeled Data—0
The Radon cumulative distribution transform and its application to image classification—0
Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing—0
Batch-normalized Maxout Network in NetworkCode0
Pooling the Convolutional Layers in Deep ConvNets for Action Recognition—0
Image classification based on support vector machine and the fusion of complementary features—0
FireCaffe: near-linear acceleration of deep neural network training on compute clusters—0
Spiking Deep Networks with LIF NeuronsCode0
Aggregating Deep Convolutional Features for Image RetrievalCode0
Layer-Specific Adaptive Learning Rates for Deep Networks—0
Deep convolutional neural networks for pedestrian detectionCode0
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Relaxed Multiple-Instance SVM with Application to Object Discovery—0
Cross-convolutional-layer Pooling for Image Recognition—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