SOTAVerified

Object Recognition

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

( Image credit: Tensorflow Object Detection API )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–950 of 2042 papers

TitleStatusHype
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets—0
Deep Learning for Material recognition: most recent advances and open challenges—0
Assessing The Importance Of Colours For CNNs In Object Recognition—0
Interpretable Graph Capsule Networks for Object Recognition—0
Unsupervised Part Discovery via Feature Alignment—0
Towards real-time object recognition and pose estimation in point clouds—0
Adversarial Attack on Facial Recognition using Visible Light—0
Insights From A Large-Scale Database of Material Depictions In Paintings—0
Complex-valued Iris Recognition Network—0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations—0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer—0
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection—0
I-POST: Intelligent Point of Sale and Transaction System—0
Adding Knowledge to Unsupervised Algorithms for the Recognition of IntentCode0
Transferred Fusion Learning using Skipped Networks—0
Out-of-Distribution Detection for Automotive Perception—0
On Numerosity of Deep Neural Networks—0
A Study of Image Pre-processing for Faster Object Recognition—0
All-Weather Object Recognition Using Radar and Infrared Sensing—0
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations—0
On the Performance of Convolutional Neural Networks under High and Low Frequency Information—0
Classifying Malware Images with Convolutional Neural Network Models—0
WaveTransform: Crafting Adversarial Examples via Input Decomposition—0
An Overview Of 3D Object Detection—0
Medical Deep Learning -- A systematic Meta-Review—0
Fit to Measure: Reasoning about Sizes for Robust Object RecognitionCode0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Object-aware Feature Aggregation for Video Object Detection—0
Boosting Gradient for White-Box Adversarial Attacks—0
Teacher-Student Consistency For Multi-Source Domain AdaptationCode0
Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks—0
Unsupervised Foveal Vision Neural Networks with Top-Down Attention—0
Dreaming with ARC—0
On the surprising similarities between supervised and self-supervised models—0
Exact neural mass model for synaptic-based working memory—0
Development of Open Informal Dataset Affecting Autonomous Driving—0
Text-Embedded Bilinear Model for Fine-Grained Visual Recognition—0
Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)—0
Deep Feature Collaboration for Challenging 3D Finger Knuckle IdentificationCode0
How does task structure shape representations in deep neural networks?—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Quantifying Adversarial Sensitivity of a Model as a Function of the Image Distribution—0
Brain-Like Object Recognition Neural Networks are more robustness to common corruptions—0
Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition—0
Visual Object Recognition in Indoor Environments Using Topologically Persistent FeaturesCode0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud—0
Learning to Represent Image and Text with Denotation Graph—0
A Study for Universal Adversarial Attacks on Texture Recognition—0
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All datasetsshape biasCIFAR10-DVSN-Caltech 101ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 GestureMECCANON-CARS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7—Unverified
2Stable Diffusionshape bias92.7—Unverified
3Partishape bias91.7—Unverified
4ViT-22B-384shape bias86.4—Unverified
5ViT-22B-560shape bias83.8—Unverified
6CLIP (ViT-B)shape bias79.9—Unverified
7ViT-22B-224shape bias78—Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5—Unverified
9ResNet-50 (with strong augmentations)shape bias62.2—Unverified
10SWSL (ResNeXt-101)shape bias49.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55—Unverified
2SSNNAccuracy (% )78.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62—Unverified
2SSNNAccuracy (% )79.25—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75—Unverified
2yunTop 5 Accuracy14.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2DYTop 5 Accuracy0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2AJ2021Top 5 Accuracy27.68—Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96—Unverified