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 1301–1350 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
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition—0
Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network—0
Knowledge-guided Semantic Computing Network—0
Deep learning systems as complex networks—0
DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING—0
Semantic Topic Analysis of Traffic Camera Images—0
Semantic and structural image segmentation for prosthetic vision—0
Learning to Localize and Align Fine-Grained Actions to Sparse Instructions—0
Transparency and Explanation in Deep Reinforcement Learning Neural Networks—0
Periocular Recognition Using CNN Features Off-the-Shelf—0
Object-sensitive Deep Reinforcement Learning—0
A Fog Robotic System for Dynamic Visual Servoing—0
Non-iterative recomputation of dense layers for performance improvement of DCNN—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM—0
AAD: Adaptive Anomaly Detection through traffic surveillance videos—0
Generalisation in humans and deep neural networksCode0
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape RecognitionCode0
VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification—0
A Domain Guided CNN Architecture for Predicting Age from Structural Brain ImagesCode0
Parsing Geometry Using Structure-Aware Shape TemplatesCode0
Saliency for Fine-grained Object Recognition in Domains with Scarce Training Data—0
Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs—0
A recurrent multi-scale approach to RBG-D Object Recognition—0
Dynamic reshaping of functional brain networks during visual 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