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 1501–1525 of 2042 papers

TitleStatusHype
Multi-View Task-Driven Recognition in Visual Sensor Networks—0
Mutual exclusivity as a challenge for deep neural networks—0
Mutual Exclusivity Loss for Semi-Supervised Deep Learning—0
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks—0
Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis—0
Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines—0
NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects?—0
NeRD: a Neural Response Divergence Approach to Visual Salience Detection—0
Nested Graph Words for Object Recognition—0
Nested Learning For Multi-Granular Tasks—0
NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage—0
Neural Image Captioning—0
NeurAll: Towards a Unified Visual Perception Model for Automated Driving—0
Neural Networks for Semantic Gaze Analysis in XR Settings—0
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans—0
Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs—0
Neurosymbolic AI - Why, What, and How—0
Neurosymbolic hybrid approach to driver collision warning—0
New Graph-based Features For Shape Recognition—0
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs—0
Noise-Adaptive Intelligent Programmable Meta-Imager—0
Non-iterative recomputation of dense layers for performance improvement of DCNN—0
Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Forests—0
Number detectors spontaneously emerge in a deep neural network designed for visual object recognition—0
Object and Text-guided Semantics for CNN-based Activity 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