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 8951–9000 of 10420 papers

TitleStatusHype
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation LearningCode1
ProxylessNAS: Direct Neural Architecture Search on Target Task and HardwareCode2
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Modern Neural Networks Generalize on Small Data Sets—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
MetaGAN: An Adversarial Approach to Few-Shot Learning—0
Symbolic Graph Reasoning Meets ConvolutionsCode0
Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform—0
Structure-Aware Convolutional Neural NetworksCode0
Stochastic Training of Residual Networks: a Differential Equation Viewpoint—0
Snapshot Distillation: Teacher-Student Optimization in One Generation—0
Classifying a specific image region using convolutional nets with an ROI mask as inputCode0
Modeling natural language emergence with integral transform theory and reinforcement learningCode0
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent—0
Graph-Based Global Reasoning NetworksCode0
Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification—0
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnessCode1
Generalized Coarse-to-Fine Visual Recognition with Progressive Training—0
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
Sequentially Aggregated Convolutional NetworksCode0
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware—0
Stochastic Gradient Push for Distributed Deep LearningCode0
Temporal Convolutional Neural Network for the Classification of Satellite Image Time SeriesCode0
Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data—0
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks—0
Evolutionary-Neural Hybrid Agents for Architecture Search—0
A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus DetectionCode0
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization—0
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation—0
Detecting Adversarial Perturbations Through Spatial Behavior in Activation Spaces—0
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial AttackCode0
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural NetworksCode0
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological SlidesCode0
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?—0
Single-Label Multi-Class Image Classification by Deep Logistic Regression—0
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging—0
Sharpen Focus: Learning with Attention Separability and ConsistencyCode0
DeepConsensus: using the consensus of features from multiple layers to attain robust image classificationCode0
Deep Discriminative Learning for Unsupervised Domain Adaptation—0
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning—0
Image Classification at Supercomputer Scale—0
DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks—0
GPipe: Efficient Training of Giant Neural Networks using Pipeline ParallelismCode2
Composite Binary Decomposition Networks—0
Exploring the Deep Feature Space of a Cell Classification Neural Network—0
Selective Feature Connection Mechanism: Concatenating Multi-layer CNN Features with a Feature Selector—0
Drop-Activation: Implicit Parameter Reduction and Harmonic RegularizationCode0
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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