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 326–350 of 2042 papers

TitleStatusHype
Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images—0
A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing—0
A Review of methods for Textureless Object Recognition—0
Agricultural Object Detection with You Look Only Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review—0
A Resilient Image Matching Method with an Affine Invariant Feature Detector and Descriptor—0
Are Labels Always Necessary for Classifier Accuracy Evaluation?—0
A Generic Regression Framework for Pose Recognition on Color and Depth Images—0
Active Gaze Behavior Boosts Self-Supervised Object Learning—0
Context Augmentation for Convolutional Neural Networks—0
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition—0
Controlled-rearing studies of newborn chicks and deep neural networks—0
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration—0
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?—0
A recurrent multi-scale approach to RBG-D Object Recognition—0
A Convolutional Neural Network based Live Object Recognition System as Blind Aid—0
Connecting metrics for shape-texture knowledge in computer vision—0
A Real-time Junk Food Recognition System based on Machine Learning—0
Are Accuracy and Robustness Correlated?—0
A concatenating framework of shortcut convolutional neural networks—0
A randomized gradient-free attack on ReLU networks—0
A Random-Fern based Feature Approach for Image Matching—0
A Framework For Refining Text Classification and Object Recognition from Academic Articles—0
3D Object Recognition with Deep Belief Nets—0
Consistency of Silhouettes and Their Duals—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—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