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 301325 of 2042 papers

TitleStatusHype
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation LearningCode1
Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions for Assembly0
Polyhedral Object Recognition by Indexing0
DAS: A Deformable Attention to Capture Salient Information in CNNs0
AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method0
Partial Coherence for Object Recognition and Depth Sensing0
Lidar Annotation Is All You NeedCode1
Selective Visual Representations Improve Convergence and Generalization for Embodied AI0
Dataset for flood area recognition with semantic segmentation0
Open-Set Object Recognition Using Mechanical Properties During Interaction0
Recognize Any RegionsCode1
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Matching the Neuronal Representations of V1 is Necessary to Improve Robustness in CNNs with V1-like Front-endsCode1
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations0
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configurationCode0
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges0
Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis0
Intriguing properties of generative classifiersCode1
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time0
Recursive Counterfactual Deconfounding for Object Recognition0
Motion Segmentation from a Moving Monocular Camera0
Algorithms for Object Detection in Substations0
Edge Aware Learning for 3D Point Cloud0
LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object RecognitionCode1
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems0
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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