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 626650 of 2042 papers

TitleStatusHype
The Neural Correlates of Image Texture in the Human Vision Using Magnetoencephalography0
ShapeY: Measuring Shape Recognition Capacity Using Nearest Neighbor Matching0
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping0
Unsupervised Spiking Instance Segmentation on Event Data using STDP0
Grassmannian learning mutual subspace method for image set recognition0
Development of collective behavior in newborn artificial agents0
Resampling and super-resolution of hexagonally sampled images using deep learning0
Certifiable Artificial Intelligence Through Data Fusion0
Investigating Negation in Pre-trained Vision-and-language ModelsCode0
Latent Cognizance: What Machine Really Learns0
HR-RCNN: Hierarchical Relational Reasoning for Object Detection0
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language ReasoningCode1
MVT: Multi-view Vision Transformer for 3D Object RecognitionCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers0
Knowledge-driven Active LearningCode0
Oriented Feature Alignment for Fine-grained Object Recognition in High-Resolution Satellite Imagery0
In Silico Modelling of Neurodegeneration Using Deep Convolutional Neural Networks0
Out-of-distribution robustness: Limited image exposure of a four-year-old is enough to outperform ResNet-500
Bio-inspired learnable divisive normalization for ANNs0
Recurrent Attention Models with Object-centric Capsule Representation for Multi-object RecognitionCode0
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model0
ZSpeedL -- Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices0
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition0
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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