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 851–875 of 2042 papers

TitleStatusHype
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks—0
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
Hierarchical Deep Learning Architecture For 10K Objects Classification—0
Global Deconvolutional Networks for Semantic Segmentation—0
Co-occurrence matrix analysis-based semi-supervised training for object detection—0
Going Deeper into Action Recognition: A Survey—0
Discriminative Ferns Ensemble for Hand Pose Recognition—0
Gradient-based Laplacian Feature Selection—0
Gradients of Counterfactuals—0
Graph-based Asynchronous Event Processing for Rapid Object Recognition—0
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition—0
Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning—0
GFCN: A New Graph Convolutional Network Based on Parallel Flows—0
Label Efficient Regularization and Propagation for Graph Node Classification—0
Graphical Gaussian Vector for Image Categorization—0
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping—0
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation—0
Grassmannian learning mutual subspace method for image set recognition—0
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection—0
Brain Inspired Face Recognition: A Computational Framework—0
A large comparison of feature-based approaches for buried target classification in forward-looking ground-penetrating radar—0
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets—0
Guided SAM: Label-Efficient Part Segmentation—0
Discrete Potts Model for Generating Superpixels on Noisy Images—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