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 1901–1950 of 2042 papers

TitleStatusHype
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition—0
Human vs. Computer in Scene and Object Recognition—0
Speech recognition in Alzheimer's disease with personal assistive robots—0
Discriminative Ferns Ensemble for Hand Pose Recognition—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Domain Adaptation on the Statistical Manifold—0
Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model—0
Analysis by Synthesis: 3D Object Recognition by Object Reconstruction—0
Recognizing RGB Images by Learning from RGB-D Data—0
Parsing Occluded People—0
Adaptive Color Attributes for Real-Time Visual Tracking—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions—0
Submodular Object Recognition—0
Anytime Recognition of Objects and Scenes—0
Persistence-based Structural Recognition—0
Learning Scalable Discriminative Dictionary with Sample Relatedness—0
Three Guidelines of Online Learning for Large-Scale Visual Recognition—0
Learning Receptive Fields for Pooling from Tensors of Feature Response—0
Merging SVMs with Linear Discriminant Analysis: A Combined Model—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—0
Combined Approach for Image Segmentation—0
Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition—0
Single camera pose estimation using Bayesian filtering and Kinect motion priorsCode0
Microsoft COCO: Common Objects in ContextCode1
PCANet: A Simple Deep Learning Baseline for Image Classification?Code0
Gradient-based Laplacian Feature Selection—0
Thoughts on a Recursive Classifier Graph: a Multiclass Network for Deep Object Recognition—0
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—0
Performance Evaluation of Raster Based Shape Vectors in Object Recognition—0
Scene Labeling with Contextual Hierarchical Models—0
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning—0
Learning Human Pose Estimation Features with Convolutional NetworksCode0
OverFeat: Integrated Recognition, Localization and Detection using Convolutional NetworksCode1
Unsupervised Feature Learning by Deep Sparse Coding—0
Using Web Co-occurrence Statistics for Improving Image Categorization—0
Some Improvements on Deep Convolutional Neural Network Based Image ClassificationCode2
Unsupervised feature learning by augmenting single images—0
Scalable Object Detection using Deep Neural NetworksCode0
Dual coordinate solvers for large-scale structural SVMs—0
Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction—0
Learning invariant representations and applications to face verification—0
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream—0
Reshaping Visual Datasets for Domain Adaptation—0
Top-Down Regularization of Deep Belief Networks—0
DeViSE: A Deep Visual-Semantic Embedding Model—0
PANDA: Pose Aligned Networks for Deep Attribute ModelingCode0
Unsupervised Learning of Invariant Representations in Hierarchical Architectures—0
Describing Textures in the WildCode1
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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