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 351–375 of 2042 papers

TitleStatusHype
A Framework for Multi-View Classification of Features—0
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations—0
A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method—0
Combinational neural network using Gabor filters for the classification of handwritten digits—0
Coloring Objects: Adjective-Noun Visual Semantic Compositionality—0
A priori compression of convolutional neural networks for wave simulators—0
A Fog Robotic System for Dynamic Visual Servoing—0
A General Ambiguity Model for Binary Edge Images with Edge Tracing and its Implementation—0
Approximation of dilation-based spatial relations to add structural constraints in neural networks—0
Collaborative Descriptors: Convolutional Maps for Preprocessing—0
Combinatorial clustering and the beta negative binomial process—0
Combined Approach for Image Segmentation—0
Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision—0
Learning Visual Models using a Knowledge Graph as a Trainer—0
Collaboration Analysis Using Deep Learning—0
A randomized gradient-free attack on ReLU networks—0
Are Accuracy and Robustness Correlated?—0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
A Real-time Junk Food Recognition System based on Machine Learning—0
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study—0
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification—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