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 826–850 of 2042 papers

TitleStatusHype
Canonical Saliency Maps: Decoding Deep Face ModelsCode0
Learning data association without data association: An EM approach to neural assignment prediction—0
IPatch: A Remote Adversarial Patch—0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked ObjectsCode0
Recurrent Feedback Improves Recognition of Partially Occluded Objects—0
SPARK: SPAcecraft Recognition leveraging Knowledge of Space Environment—0
Rock Hunting With Martian Machine Vision—0
Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection—0
Tuned Compositional Feature Replays for Efficient Stream LearningCode0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
Training Deep Neural Networks via Branch-and-BoundCode0
A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis—0
CNN-based search model underestimates attention guidance by simple visual features—0
Domain-robust VQA with diverse datasets and methods but no target labels—0
VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation—0
Projection: A Mechanism for Human-like Reasoning in Artificial Intelligence—0
DNN Quantization with Attention—0
Contrastive Reasoning in Neural Networks—0
Neural Networks for Semantic Gaze Analysis in XR Settings—0
Hebbian Semi-Supervised Learning in a Sample Efficiency Setting—0
Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation—0
Structure-From-Motion and RGBD Depth Fusion—0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
A Discriminative Vectorial Framework for Multi-modal Feature Representation—0
Offboard 3D Object Detection from Point Cloud Sequences—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