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 1101–1125 of 2042 papers

TitleStatusHype
Quantum Doubly Stochastic Transformers—0
Query Adaptive Similarity Measure for RGB-D Object Recognition—0
Radar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units—0
RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline—0
Randomized Kernel Multi-view Discriminant Analysis—0
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality—0
RAPTOR: Refined Approach for Product Table Object Recognition—0
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing—0
Object-Size-Driven Design of Convolutional Neural Networks: Virtual Axle Detection based on Raw Data—0
RAZER: Robust Accelerated Zero-Shot 3D Open-Vocabulary Panoptic Reconstruction with Spatio-Temporal Aggregation—0
Reading Ancient Coin Legends: Object Recognition vs. OCR—0
Achieving More Human Brain-Like Vision via Human EEG Representational Alignment—0
Real-Time 3D Occupancy Prediction via Geometric-Semantic Disentanglement—0
Real-time Monocular Object SLAM—0
Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach—0
Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?—0
Reason from Context with Self-supervised Learning—0
Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition—0
Recognizing Objects From Any View With Object and Viewer-Centered Representations—0
Recognizing Objects In-the-wild: Where Do We Stand?—0
Recognizing Open-Vocabulary Relations between Objects in Images—0
Recognizing RGB Images by Learning from RGB-D Data—0
Recurrent 3D Attentional Networks for End-to-End Active Object Recognition—0
Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders—0
Recurrent Connectivity Aids Recognition of Partly Occluded Objects—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