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

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Showing 1451–1500 of 2042 papers

TitleStatusHype
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields—0
Graph Convolutional Networks for Classification with a Structured Label Space—0
Eigen-Distortions of Hierarchical Representations—0
A concatenating framework of shortcut convolutional neural networks—0
Understanding Low- and High-Level Contributions to Fixation Prediction—0
Deep Scene Image Classification With the MFAFVNet—0
Deep Competitive Pathway NetworksCode0
Are we done with object recognition? The iCub robot's perspectiveCode0
A Generic Regression Framework for Pose Recognition on Color and Depth Images—0
Combinational neural network using Gabor filters for the classification of handwritten digits—0
Recognizing Objects In-the-wild: Where Do We Stand?—0
Object Recognition from very few Training Examples for Enhancing Bicycle Maps—0
Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions—0
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation—0
Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach—0
Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition—0
Distributed Deep Neural Networks over the Cloud, the Edge and End DevicesCode1
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable—0
ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids—0
ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification—0
Automatic Dataset Augmentation—0
One-Shot Concept Learning by Simulating Evolutionary Instinct Development—0
CNN Fixations: An unraveling approach to visualize the discriminative image regionsCode0
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects—0
Knock-Knock: Acoustic Object Recognition by using Stacked Denoising Autoencoders—0
Chessboard and chess piece recognition with the support of neural networksCode0
Belief Tree Search for Active Object Recognition—0
Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning—0
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection—0
Capacity limitations of visual search in deep convolutional neural networks—0
Analysis and Optimization of Convolutional Neural Network ArchitecturesCode0
Fashioning with Networks: Neural Style Transfer to Design Clothes—0
An Effective Training Method For Deep Convolutional Neural Network—0
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs—0
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach—0
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture—0
Improving Gibbs Sampler Scan Quality with DoGS—0
Fast Feature Fool: A data independent approach to universal adversarial perturbationsCode0
Robust Visual Tracking via Hierarchical Convolutional FeaturesCode0
Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition—0
Deep Discrete Hashing with Self-supervised Pairwise LabelsCode0
Exploration of object recognition from 3D point cloud—0
Model compression as constrained optimization, with application to neural nets. Part I: general framework—0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation—0
Deep Co-Occurrence Feature Learning for Visual Object RecognitionCode0
AGA: Attribute-Guided AugmentationCode0
BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition—0
Missing Modalities Imputation via Cascaded Residual Autoencoder—0
Obtaining referential word meanings from visual and distributional information: Experiments on object naming—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