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 901–925 of 2042 papers

TitleStatusHype
Hierarchical Novelty Detection for Visual Object Recognition—0
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition—0
Hierarchical Piecewise-Constant Super-regions—0
Hierarchical Prototype Learning for Zero-Shot Recognition—0
Data-driven Verification of DNNs for Object Recognition—0
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition—0
Highlight Timestamp Detection Model for Comedy Videos via Multimodal Sentiment Analysis—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation—0
How Can CNNs Use Image Position for Segmentation?—0
Image Captioning with Unseen Objects—0
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses—0
ImageNet MPEG-7 Visual Descriptors - Technical Report—0
How good are deep models in understanding the generated images?—0
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time—0
DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell Classification with LIME-Based Explainability—0
How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning—0
How to deal with glare for improved perception of Autonomous Vehicles—0
I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data—0
How well do deep neural networks trained on object recognition characterize the mouse visual system?—0
Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study—0
HR-RCNN: Hierarchical Relational Reasoning for Object Detection—0
Human Action Recognition in Still Images Using ConViT—0
Fractional order graph neural network—0
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning—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