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 9501–9550 of 10420 papers

TitleStatusHype
Discriminative and Geometry Aware Unsupervised Domain Adaptation—0
Large-Scale 3D Scene Classification With Multi-View Volumetric CNN—0
A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading—0
Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications—0
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition—0
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted LabelsCode0
A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image ClassificationCode0
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video ClassificationCode0
The Effectiveness of Data Augmentation in Image Classification using Deep LearningCode0
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer—0
Predicting Yelp Star Reviews Based on Network Structure with Deep LearningCode0
StrassenNets: Deep Learning with a Multiplication BudgetCode0
An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image ClassificationCode0
Gradient Normalization & Depth Based Decay For Deep Learning—0
Basic Thresholding Classification—0
Defense against Adversarial Attacks Using High-Level Representation Guided DenoiserCode0
In-Place Activated BatchNorm for Memory-Optimized Training of DNNsCode0
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing—0
Maximum Classifier Discrepancy for Unsupervised Domain AdaptationCode1
Deep Gradient Compression Reduce the Communication Bandwidth For distributed TraningCode0
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep LearningCode0
What's in my closet?: Image classification using fuzzy logic—0
Successive Embedding and Classification Loss for Aerial Image ClassificationCode0
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingCode0
Raw Waveform-based Audio Classification Using Sample-level CNN Architectures—0
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root NormalizationCode0
Triagem virtual de imagens de imuno-histoquímica usando redes neurais artificiais e espectro de padrões—0
Fuzzy-Based Dialectical Non-Supervised Image Classification and Clustering—0
Progressive Neural Architecture SearchCode0
Improving End-to-End Memory Networks with Unified Weight Tying—0
Learning to Model the Tail—0
Gated Recurrent Convolution Neural Network for OCRCode0
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases—0
Spatially-Adaptive Filter Units for Deep Neural NetworksCode0
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial NetworksCode0
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Code0
Exploiting Nontrivial Connectivity for Automatic Speech Recognition—0
Between-class Learning for Image ClassificationCode0
On the Robustness of Semantic Segmentation Models to Adversarial AttacksCode0
An Introduction to Deep Visual Explanation—0
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks—0
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial ConvolutionsCode0
Context Augmentation for Convolutional Neural Networks—0
Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN—0
Tactics to Directly Map CNN graphs on Embedded FPGAsCode0
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
Using KL-divergence to focus Deep Visual Explanation—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
Less-forgetful Learning for Domain Expansion in Deep Neural Networks—0
Zero-Shot Learning via Category-Specific Visual-Semantic Mapping—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