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 7601–7650 of 10420 papers

TitleStatusHype
Improved Gradient based Adversarial Attacks for Quantized NetworksCode0
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification—0
Applications of the Streaming Networks—0
GAN-based Priors for Quantifying UncertaintyCode1
Convolutional Spiking Neural Networks for Spatio-Temporal Feature ExtractionCode1
Strategies for Robust Image Classification—0
Classification of Chinese Handwritten Numbers with Labeled Projective Dictionary Pair Learning—0
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features—0
Milking CowMask for Semi-Supervised Image ClassificationCode0
Triad State Space Construction for Chaotic Signal Classification with Deep Learning—0
Hit-Detector: Hierarchical Trinity Architecture Search for Object DetectionCode1
Pipelined Backpropagation at Scale: Training Large Models without Batches—0
Circumventing Outliers of AutoAugment with Knowledge DistillationCode1
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?Code1
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks—0
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet—0
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning—0
Robust and On-the-fly Dataset Denoising for Image Classification—0
Surface Damage Detection Scheme using Convolutional Neural Network and Artificial Neural Network—0
Meta Pseudo LabelsCode1
Performance Evaluation of Low-Cost Machine Vision Cameras for Image-Based Grasp VerificationCode0
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection—0
TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks—0
HDF: Hybrid Deep Features for Scene Image Representation—0
Adversarial Continual LearningCode1
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning—0
Adversarial Robustness on In- and Out-Distribution Improves ExplainabilityCode1
Fine-grained Species Recognition with Privileged Pooling: Better Sample Efficiency Through Supervised AttentionCode0
Event-Based Control for Online Training of Neural Networks—0
Efficient Deep Representation Learning by Adaptive Latent Space Sampling—0
Affinity Graph Supervision for Visual Recognition—0
Ensemble learning in CNN augmented with fully connected subnetworksCode0
Overinterpretation reveals image classification model pathologiesCode1
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units—0
A Dynamic Reduction Network for Point CloudsCode0
Fixing the train-test resolution discrepancy: FixEfficientNetCode2
Teacher-Student chain for efficient semi-supervised histology image classification—0
Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis—0
SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking—0
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic SegmentationCode2
Assessing Robustness to Noise: Low-Cost Head CT Triage—0
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial LocationCode1
SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Weights Flipping—0
Synthesizing human-like sketches from natural images using a conditional convolutional decoderCode1
Developing a Recommendation Benchmark for MLPerf Training and Inference—0
Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks—0
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models—0
Stochastic gradient descent with random learning rate—0
A Simple Probabilistic Method for Deep Classification under Input-Dependent Label Noise—0
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
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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