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 1601–1650 of 2042 papers

TitleStatusHype
An online passive-aggressive algorithm for difference-of-squares classification—0
A Non-Technical Survey on Deep Convolutional Neural Network Architectures—0
An optical biomimetic eyes with interested object imaging—0
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity—0
A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis—0
A Novel Equation based Classifier for Detecting Human in Images—0
A Novel Explainable Artificial Intelligence Model in Image Classification problem—0
A Novel Feature Extraction Method for Scene Recognition Based on Centered Convolutional Restricted Boltzmann Machines—0
A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations—0
A Novel Locally Linear KNN Model for Visual Recognition—0
A Novel mapping for visual to auditory sensory substitution—0
An Overview Of 3D Object Detection—0
Answer-Type Prediction for Visual Question Answering—0
Anytime Recognition of Objects and Scenes—0
A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching—0
A Poodle or a Dog? Evaluating Automatic Image Annotation Using Human Descriptions at Different Levels of Granularity—0
Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras—0
Application of Deep Learning in Generating Desired Design Options: Experiments Using Synthetic Training Dataset—0
Application of Faster R-CNN model on Human Running Pattern Recognition—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification—0
Approximation of dilation-based spatial relations to add structural constraints in neural networks—0
A priori compression of convolutional neural networks for wave simulators—0
A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
A Random-Fern based Feature Approach for Image Matching—0
A randomized gradient-free attack on ReLU networks—0
Are Accuracy and Robustness Correlated?—0
A Real-time Junk Food Recognition System based on Machine Learning—0
A recurrent multi-scale approach to RBG-D Object Recognition—0
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?—0
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration—0
Are Labels Always Necessary for Classifier Accuracy Evaluation?—0
A Resilient Image Matching Method with an Affine Invariant Feature Detector and Descriptor—0
A Review of methods for Textureless Object Recognition—0
A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing—0
A Review on Near Duplicate Detection of Images using Computer Vision Techniques—0
Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection—0
Artistic Object Recognition by Unsupervised Style Adaptation—0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling—0
Artwork Recognition for Panorama Images Based on Optimized ASIFT and Cubic Projection—0
Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos—0
A Self-supervised GAN for Unsupervised Few-shot Object Recognition—0
A semantics-driven methodology for high-quality image annotation—0
A shallow residual neural network to predict the visual cortex response—0
A ``Shape Aware'' Model for semi-supervised Learning of Objects and its Context—0
A Simulation Method for MMW Radar Sensing in Traffic Intersection Based on BART Algorithm—0
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation—0
A Spike Learning System for Event-driven Object 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