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 1576–1600 of 2042 papers

TitleStatusHype
Analysis by Synthesis: 3D Object Recognition by Object Reconstruction—0
Analysis of Dropout in Online Learning—0
Analysis of dropout learning regarded as ensemble learning—0
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?—0
Analyzing structural characteristics of object category representations from their semantic-part distributions—0
Analyzing the Dependency of ConvNets on Spatial Information—0
Analyzing the Performance of Multilayer Neural Networks for Object Recognition—0
An Application-Specific VLIW Processor with Vector Instruction Set for CNN Acceleration—0
An Approach for Noise Removal on Depth Images—0
An ecologically motivated image dataset for deep learning yields better models of human vision—0
An Effective Training Method For Deep Convolutional Neural Network—0
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks—0
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis—0
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation—0
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement—0
A Neuro-AI Interface: Learning DNNs from the Human Brain—0
A neuromorphic approach to image processing and machine vision—0
A newborn embodied Turing test for view-invariant object recognition—0
A new GAN-based anomaly detection (GBAD) approach for multi-threat object classification on large-scale x-ray security images—0
A New Manifold Distance Measure for Visual Object Categorization—0
A New Urban Objects Detection Framework Using Weakly Annotated Sets—0
Angular Luminance for Material Segmentation—0
Annotation of Online Shopping Images without Labeled Training Examples—0
Anomaly Detection with Domain Adaptation—0
An online passive-aggressive algorithm for difference-of-squares classification—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