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 801–825 of 2042 papers

TitleStatusHype
SISA: Securing Images by Selective Alteration—0
Place recognition survey: An update on deep learning approaches—0
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks—0
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition—0
The role of temporal cortex in the control of attention—0
Hidden Patch Attacks for Optical Flow—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
A Novel mapping for visual to auditory sensory substitution—0
Modeling Object Recognition in Newborn Chicks using Deep Neural Networks—0
Learning the Precise Feature for Cluster AssignmentCode0
On the role of feedback in visual processing: a predictive coding perspectiveCode0
A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing—0
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains—0
Statistical Mechanics of Neural Processing of Object Manifolds—0
A Voxel Graph CNN for Object Classification with Event Cameras—0
Highlight Timestamp Detection Model for Comedy Videos via Multimodal Sentiment Analysis—0
GuideMe: A Mobile Application based on Global Positioning System and Object Recognition Towards a Smart Tourist Guide—0
Weakly Supervised Instance Attention for Multisource Fine-Grained Object Recognition with an Application to Tree Species Classification—0
An online passive-aggressive algorithm for difference-of-squares classification—0
Opening Deep Neural Networks with Generative ModelsCode0
Brain Inspired Face Recognition: A Computational Framework—0
SizeNet: Object Recognition via Object Real Size-based Convolutional Networks—0
ORCEA: Object Recognition by Continuous Evidence Assimilation—0
BIM Hyperreality: Data Synthesis Using BIM and Hyperrealistic Rendering for Deep Learning—0
Modelling of LIDAR sensor disturbances by solid airborne particles—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