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
Are Accuracy and Robustness Correlated?—0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Global Deconvolutional Networks for Semantic Segmentation—0
Exploration of object recognition from 3D point cloud—0
Going Deeper into Action Recognition: A Survey—0
A randomized gradient-free attack on ReLU networks—0
Gradient-based Laplacian Feature Selection—0
Gradients of Counterfactuals—0
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
Graph-Based High-Order Relation Discovery for Fine-Grained Recognition—0
Exploiting Temporal Relations on Radar Perception for Autonomous Driving—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
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
Grassmannian learning mutual subspace method for image set recognition—0
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation—0
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision—0
A Random-Fern based Feature Approach for Image Matching—0
A Framework For Refining Text Classification and Object Recognition from Academic Articles—0
Guided SAM: Label-Efficient Part Segmentation—0
A concatenating framework of shortcut convolutional neural 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