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 1451–1475 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
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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