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

Showing 826850 of 2042 papers

TitleStatusHype
Assessing The Importance Of Colours For CNNs In Object Recognition0
Full-Glow: Fully conditional Glow for more realistic image generationCode1
The Lottery Ticket Hypothesis for Object RecognitionCode1
Interpretable Graph Capsule Networks for Object Recognition0
Unsupervised Part Discovery via Feature Alignment0
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image PerturbationsCode1
Towards real-time object recognition and pose estimation in point clouds0
Sparse R-CNN: End-to-End Object Detection with Learnable ProposalsCode2
Adversarial Attack on Facial Recognition using Visible Light0
Insights From A Large-Scale Database of Material Depictions In Paintings0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG190
Complex-valued Iris Recognition Network0
Enriching ImageNet with Human Similarity Judgments and Psychological EmbeddingsCode1
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification0
A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer0
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object RecognitionCode1
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection0
I-POST: Intelligent Point of Sale and Transaction System0
Adding Knowledge to Unsupervised Algorithms for the Recognition of IntentCode0
Transferred Fusion Learning using Skipped Networks0
Out-of-Distribution Detection for Automotive Perception0
On Numerosity of Deep Neural Networks0
A Study of Image Pre-processing for Faster Object Recognition0
On the Performance of Convolutional Neural Networks under High and Low Frequency Information0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified