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 1326–1350 of 2042 papers

TitleStatusHype
IPatch: A Remote Adversarial Patch—0
I-POST: Intelligent Point of Sale and Transaction System—0
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report—0
ISCUTE: Instance Segmentation of Cables Using Text Embedding—0
Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks—0
ISP Distillation—0
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence—0
Joint Deep Learning for Car Detection—0
KerCNNs: biologically inspired lateral connections for classification of corrupted images—0
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification—0
Kernel Null Space Methods for Novelty Detection—0
Kernel principal component analysis network for image classification—0
Kitting in the Wild through Online Domain Adaptation—0
KNN-Based Automatic Cropping for Improved Threat Object Recognition in X-Ray Security Images—0
Knock-Knock: Acoustic Object Recognition by using Stacked Denoising Autoencoders—0
Knowledge-guided Semantic Computing Network—0
L3DOC: Lifelong 3D Object Classification—0
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks—0
Label Efficient Learning of Transferable Representations across Domains and Tasks—0
Language Models as Zero-shot Visual Semantic Learners—0
Large Margin Low Rank Tensor Analysis—0
Large Scale Distributed Deep Networks—0
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding—0
Large Scale Long-tailed Product Recognition System at Alibaba—0
Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision—0
Show:102550
← PrevPage 54 of 82Next →
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