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 1626–1650 of 2042 papers

TitleStatusHype
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
Show:102550
← PrevPage 66 of 82Next →
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