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 1676–1700 of 2042 papers

TitleStatusHype
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
Recurrent Convolutional Neural Network for Object Recognition—0
Recurrent Feedback Improves Recognition of Partially Occluded Objects—0
Generalized Relevance Learning Grassmann QuantizationCode0
Generate To Adapt: Aligning Domains using Generative Adversarial NetworksCode0
A Probabilistic Theory of Deep LearningCode0
3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning ModelsCode0
Parsing Geometry Using Structure-Aware Shape TemplatesCode0
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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