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 1951–1975 of 2042 papers

TitleStatusHype
Unsupervised Learning of Invariant Representations in Hierarchical Architectures—0
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks—0
Object Recognition System Design in Computer Vision: a Universal Approach—0
DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionCode0
Image Description using Visual Dependency Representations—0
Surface Registration Using Genetic Algorithm in Reduced Search Space—0
Suspicious Object Recognition Method in Video Stream Based on Visual Attention—0
MonoStream: A Minimal-Hardware High Accuracy Device-free WLAN Localization System—0
Visual Features for Linguists: Basic image analysis techniques for multimodally-curious NLPers—0
Domain-invariant Face Recognition using Learned Low-rank Transformation—0
VSEM: An open library for visual semantics representation—0
Attribute-Based Classification for Zero-Shot Visual Object Categorization—0
Integration of 3D Object Recognition and Planning for Robotic Manipulation: A Preliminary Report—0
A Novel Equation based Classifier for Detecting Human in Images—0
Introducing Memory and Association Mechanism into a Biologically Inspired Visual Model—0
iCub World: Friendly Robots Help Building Good Vision Data-Sets—0
Matching objects across the textured-smooth continuum—0
Feature Learning by Multidimensional Scaling and its Applications in Object RecognitionCode0
The Ripple Pond: Enabling Spiking Networks to See—0
Large Margin Low Rank Tensor Analysis—0
Fully-Connected CRFs with Non-Parametric Pairwise Potential—0
Learning and Calibrating Per-Location Classifiers for Visual Place Recognition—0
Kernel Null Space Methods for Novelty Detection—0
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images—0
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition—0
Show:102550
← PrevPage 79 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