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 1001–1025 of 2042 papers

TitleStatusHype
Fast Training of Deep Networks with One-Class CNNs—0
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation ProcessCode0
Teaching CNNs to mimic Human Visual Cognitive Process & regularise Texture-Shape biasCode0
Hierarchically Compositional Tasks and Deep Convolutional Networks—0
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation—0
Object recognition through pose and shape estimation—0
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition—0
Interpretable multimodal fusion networks reveal mechanisms of brain cognition—0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data—0
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks—0
Image Enhancement and Object Recognition for Night Vision Surveillance—0
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks—0
What takes the brain so long: Object recognition at the level of minimal images develops for up to seconds of presentation time—0
Training Deep Spiking Neural Networks—0
Information Mandala: Statistical Distance Matrix with Clustering—0
Anomaly Detection with Domain Adaptation—0
2D Image Features Detector And Descriptor Selection Expert System—0
Recognizing Objects From Any View With Object and Viewer-Centered Representations—0
Adversarial Attacks and Defense on Texts: A Survey—0
Object-QA: Towards High Reliable Object Quality Assessment—0
End-to-End Auditory Object Recognition via Inception Nucleus—0
Misalignment Resilient Diffractive Optical Networks—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