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 601–625 of 2042 papers

TitleStatusHype
Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?—0
BSED: Baseline Shapley-Based Explainable Detector—0
Domain Adaptation on the Statistical Manifold—0
Domain Adaptive Neural Networks for Object Recognition—0
Building Machines That Learn and Think Like People—0
Domain Generalization: A Survey—0
Bridging the Gap Between Computational Photography and Visual Recognition—0
Adversarial Attack on Facial Recognition using Visible Light—0
Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks—0
A New Urban Objects Detection Framework Using Weakly Annotated Sets—0
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing—0
Domain-invariant Face Recognition using Learned Low-rank Transformation—0
Domain-Invariant Proposals based on a Balanced Domain Classifier for Object Detection—0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer—0
DNN Quantization with Attention—0
Can Boosting with SVM as Week Learners Help?—0
Can domain adaptation make object recognition work for everyone?—0
1 Million Captioned Dutch Newspaper Images—0
Do semantic parts emerge in Convolutional Neural Networks?—0
Diversity in Object Proposals—0
Do We Need More Training Data?—0
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments—0
Dreaming with ARC—0
DTM: Deformable Template Matching—0
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition—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