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 1851–1900 of 2042 papers

TitleStatusHype
Joint Deep Learning for Car Detection—0
Multiple Object Recognition with Visual AttentionCode0
Unsupervised Feature Learning with C-SVDDNet—0
Striving for Simplicity: The All Convolutional NetCode0
Self-informed neural network structure learning—0
Training Deep Neural Networks on Noisy Labels with BootstrappingCode1
An Analysis of Unsupervised Pre-training in Light of Recent AdvancesCode0
Towards Open World Recognition—0
Compressing Deep Convolutional Networks using Vector Quantization—0
Object Recognition Using Deep Neural Networks: A Survey—0
Theano-based Large-Scale Visual Recognition with Multiple GPUsCode0
Deep Symmetry Networks—0
Zero-shot recognition with unreliable attributes—0
Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksCode0
Learning Deep Features for Scene Recognition using Places Database—0
Self-Adaptable Templates for Feature Coding—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation—0
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors—0
Visual Sentiment Prediction with Deep Convolutional Neural Networks—0
Maximum Likelihood Directed Enumeration Method in Piecewise-Regular Object Recognition—0
Sparse distributed localized gradient fused features of objects—0
Zero-Aliasing Correlation Filters for Object Recognition—0
Abnormal Object Recognition: A Comprehensive Study—0
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNetCode1
Generalized Adaptive Dictionary Learning via Domain Shift Minimization—0
A hierarchical framework for object recognition—0
Learning visual biases from human imagination—0
Efficient Image Categorization with Sparse Fisher Vector—0
Zero-Shot Object Recognition System based on Topic Model—0
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual RecognitionCode0
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics—0
1-HKUST: Object Detection in ILSVRC 2014—0
Domain Adaptive Neural Networks for Object Recognition—0
Going Deeper with ConvolutionsCode1
Transferring Landmark Annotations for Cross-Dataset Face Alignment—0
ImageNet Large Scale Visual Recognition ChallengeCode1
Learning Multi-Scale Representations for Material Classification—0
Coloring Objects: Adjective-Noun Visual Semantic Compositionality—0
A Poodle or a Dog? Evaluating Automatic Image Annotation Using Human Descriptions at Different Levels of Granularity—0
See No Evil, Say No Evil: Description Generation from Densely Labeled Images—0
Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Forests—0
Object Proposal Generation using Two-Stage Cascade SVMs—0
Pixels to Voxels: Modeling Visual Representation in the Human Brain—0
What you need to know about the state-of-the-art computational models of object-vision: A tour through the models—0
Analyzing the Performance of Multilayer Neural Networks for Object Recognition—0
Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach—0
Face Identification with Second-Order Pooling—0
3D ShapeNets: A Deep Representation for Volumetric ShapesCode1
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionCode0
Show:102550
← PrevPage 38 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