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 2001–2042 of 2042 papers

TitleStatusHype
Learning with Recursive Perceptual Representations—0
Timely Object Recognition—0
Discriminatively Trained Sparse Code Gradients for Contour Detection—0
Semantic Kernel Forests from Multiple Taxonomies—0
Graphical Gaussian Vector for Image Categorization—0
Unsupervised Template Learning for Fine-Grained Object Recognition—0
Learning about Canonical Views from Internet Image Collections—0
Efficient Point-to-Subspace Query in ^1 with Application to Robust Object Instance Recognition—0
Improving neural networks by preventing co-adaptation of feature detectorsCode1
Spectral Dependency Parsing with Latent Variables—0
UWN: A Large Multilingual Lexical Knowledge Base—0
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding—0
Detecting Visual Text—0
HRItk: The Human-Robot Interaction ToolKit Rapid Development of Speech-Centric Interactive Systems in ROS—0
Describing Video Contents in Natural Language—0
Learning to Learn with Compound HD Models—0
PiCoDes: Learning a Compact Code for Novel-Category Recognition—0
Why The Brain Separates Face Recognition From Object Recognition—0
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition—0
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition—0
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning—0
Boosting with Maximum Adaptive Sampling—0
Combinatorial clustering and the beta negative binomial process—0
Nested Graph Words for Object Recognition—0
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction—0
A biologically plausible network for the computation of orientation dominance—0
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations—0
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification—0
Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition—0
Tiled convolutional neural networks—0
Using body-anchored priors for identifying actions in single images—0
(RF)^2 -- Random Forest Random Field—0
3D Object Recognition with Deep Belief Nets—0
Learning in Markov Random Fields using Tempered Transitions—0
On Invariance in Hierarchical Models—0
An Additive Latent Feature Model for Transparent Object Recognition—0
Learning a discriminative hidden part model for human action recognition—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree—0
A ``Shape Aware'' Model for semi-supervised Learning of Objects and its Context—0
The Distribution Family of Similarity Distances—0
Distinctive Image Features from Scale-Invariant KeypointsCode0
Show:102550
← PrevPage 41 of 41Next →
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