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 1126–1150 of 2042 papers

TitleStatusHype
Recurrent Convolutional Neural Network for Object Recognition—0
Recurrent Feedback Improves Recognition of Partially Occluded Objects—0
Recursive Counterfactual Deconfounding for Object Recognition—0
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction—0
Reducing Bias in Production Speech Models—0
Reducing Label Effort: Self-Supervised meets Active Learning—0
Reducing Overconfidence Predictions for Autonomous Driving Perception—0
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception—0
Refining Neural Activation Patterns for Layer-Level Concept Discovery in Neural Network-Based Receivers—0
Reflection Invariant and Symmetry Detection—0
Regional Active Contours based on Variational level sets and Machine Learning for Image Segmentation—0
Region-Manipulated Fusion Networks for Pancreatitis Recognition—0
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search—0
Regularizing Max-Margin Exemplars by Reconstruction and Generative Models—0
Regulation of Mouse Learning and Mood by the Anti-Inflammatory Cytokine Interleukin-10—0
Relative Afferent Pupillary Defect Screening through Transfer Learning—0
Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers—0
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering—0
Representational constraints underlying similarity between task-optimized neural systems—0
Representation Learning on Unit Ball with 3D Roto-Translational Equivariance—0
Resampling and super-resolution of hexagonally sampled images using deep learning—0
ResearchDoom and CocoDoom: Learning Computer Vision with Games—0
Reshaping Visual Datasets for Domain Adaptation—0
Resume Information Extraction via Post-OCR Text Processing—0
Revealing Bias Formation in Deep Neural Networks Through the Geometric Mechanisms of Human Visual Decoupling—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