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
Learning Descriptors for Object Recognition and 3D Pose Estimation—0
Scalable Bayesian Optimization Using Deep Neural NetworksCode0
Unsupervised Network Pretraining via Encoding Human Design—0
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework—0
DeepID3: Face Recognition with Very Deep Neural NetworksCode0
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection—0
Hyper-parameter optimization of Deep Convolutional Networks for object recognition—0
Deep Learning with Nonparametric ClusteringCode0
A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video—0
From Visual Attributes to Adjectives through Decompositional Distributional Semantics—0
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
An Analysis of Unsupervised Pre-training in Light of Recent AdvancesCode0
Compressing Deep Convolutional Networks using Vector Quantization—0
Towards Open World Recognition—0
Object Recognition Using Deep Neural Networks: A Survey—0
Theano-based Large-Scale Visual Recognition with Multiple GPUsCode0
Self-Adaptable Templates for Feature Coding—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
Zero-shot recognition with unreliable attributes—0
Learning Deep Features for Scene Recognition using Places Database—0
Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksCode0
Deep Symmetry Networks—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
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
Transferring Landmark Annotations for Cross-Dataset Face Alignment—0
Learning Multi-Scale Representations for Material Classification—0
See No Evil, Say No Evil: Description Generation from Densely Labeled Images—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
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
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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