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 1701–1725 of 2042 papers

TitleStatusHype
BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition—0
Bio-inspired learnable divisive normalization for ANNs—0
Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition—0
BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once—0
Block-wise Scrambled Image Recognition Using Adaptation Network—0
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations—0
Boosting Gradient for White-Box Adversarial Attacks—0
Boosting Object Recognition in Point Clouds by Saliency Detection—0
Boosting with Maximum Adaptive Sampling—0
BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition—0
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning—0
Brain Cancer Segmentation Using YOLOv5 Deep Neural Network—0
Brain Inspired Face Recognition: A Computational Framework—0
Brain-Like Object Recognition Neural Networks are more robustness to common corruptions—0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections—0
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition—0
Bridging the Gap Between Computational Photography and Visual Recognition—0
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation—0
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture—0
BSED: Baseline Shapley-Based Explainable Detector—0
Building a visual semantics aware object hierarchy—0
Building Machines That Learn and Think Like People—0
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing—0
CAggNet: Crossing Aggregation Network for Medical Image Segmentation—0
Show:102550
← PrevPage 69 of 82Next →
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