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 1476–1500 of 2042 papers

TitleStatusHype
More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition—0
More Words and Bigger Pictures—0
MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos—0
Motion Equivariance OF Event-based Camera Data with the Temporal Normalization Transform—0
Motion Mapping Cognition: A Nondecomposable Primary Process in Human Vision—0
MotionRec: A Unified Deep Framework for Moving Object Recognition—0
Motion Segmentation from a Moving Monocular Camera—0
Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions for Assembly—0
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition—0
Multi-label Object Attribute Classification using a Convolutional Neural Network—0
Multilevel Context Representation for Improving Object Recognition—0
Multimodal Skip-gram Using Convolutional Pseudowords—0
Multiple Class Novelty Detection Under Data Distribution Shift—0
Multiple Instance Learning Convolutional Neural Networks for Object Recognition—0
Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning—0
Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach—0
Multipod Convolutional Network—0
Multiscale Adaptive Representation of Signals: I. The Basic Framework—0
Multi-Scale Saliency Detection using Dictionary Learning—0
Multi-Scale Weight Sharing Network for Image Recognition—0
Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery—0
Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring—0
Multi-View Harmonized Bilinear Network for 3D 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