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 1901–1925 of 2042 papers

TitleStatusHype
What you need to know about the state-of-the-art computational models of object-vision: A tour through the models—0
Analyzing the Performance of Multilayer Neural Networks for Object Recognition—0
Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach—0
Face Identification with Second-Order Pooling—0
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionCode0
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition—0
Analysis by Synthesis: 3D Object Recognition by Object Reconstruction—0
Persistence-based Structural Recognition—0
Discriminative Ferns Ensemble for Hand Pose Recognition—0
Human vs. Computer in Scene and Object Recognition—0
Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model—0
Submodular Object Recognition—0
Speech recognition in Alzheimer's disease with personal assistive robots—0
Learning Scalable Discriminative Dictionary with Sample Relatedness—0
RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions—0
Merging SVMs with Linear Discriminant Analysis: A Combined Model—0
Adaptive Color Attributes for Real-Time Visual Tracking—0
Three Guidelines of Online Learning for Large-Scale Visual Recognition—0
Parsing Occluded People—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
Domain Adaptation on the Statistical Manifold—0
Recognizing RGB Images by Learning from RGB-D Data—0
Anytime Recognition of Objects and Scenes—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—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