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 1301–1325 of 2042 papers

TitleStatusHype
Using Motion and Internal Supervision in Object Recognition—0
Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions—0
Grounded Human-Object Interaction Hotspots from VideoCode0
Spectral Illumination Correction: Achieving Relative Color Constancy Under the Spectral DomainCode0
Global Second-order Pooling Convolutional NetworksCode0
A randomized gradient-free attack on ReLU networks—0
A Convolutional Neural Network based Live Object Recognition System as Blind Aid—0
Task-generalizable Adversarial Attack based on Perceptual MetricCode0
Sequence-based Person Attribute Recognition with Joint CTC-Attention Model—0
Artificial Color Constancy via GoogLeNet with Angular Loss FunctionCode0
CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humans—0
PydMobileNet: Improved Version of MobileNets with Pyramid Depthwise Separable ConvolutionCode0
Application of Faster R-CNN model on Human Running Pattern Recognition—0
A Framework of Transfer Learning in Object Detection for Embedded SystemsCode0
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural NetworkCode0
Multi-label Object Attribute Classification using a Convolutional Neural Network—0
The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance—0
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?—0
Object Detection based on LIDAR Temporal Pulses using Spiking Neural Networks—0
UAVid: A Semantic Segmentation Dataset for UAV ImageryCode0
Improving Annotation for 3D Pose Dataset of Fine-Grained Object CategoriesCode0
CURE-OR: Challenging Unreal and Real Environments for Object RecognitionCode0
The Newton Scheme for Deep Learning—0
Point Cloud GANCode0
Efficient architecture for deep neural networks with heterogeneous sensitivity—0
Show:102550
← PrevPage 53 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