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 1951–2000 of 2042 papers

TitleStatusHype
End-to-End Learning of Representations for Asynchronous Event-Based DataCode0
Artificial Color Constancy via GoogLeNet with Angular Loss FunctionCode0
One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial NetworkCode0
One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal PerturbationCode0
Are we done with object recognition? The iCub robot's perspectiveCode0
Triplet-Center Loss for Multi-View 3D Object RetrievalCode0
Complementary datasets to COCO for object detectionCode0
In-distribution adversarial attacks on object recognition models using gradient-free searchCode0
Learning Human Pose Estimation Features with Convolutional NetworksCode0
Towards Interpreting Recurrent Neural Networks through Probabilistic AbstractionCode0
Comparing deep neural networks against humans: object recognition when the signal gets weakerCode0
Learning Inductive Biases with Simple Neural NetworksCode0
Topology-Guided Knowledge Distillation for Efficient Point Cloud ProcessingCode0
ReNet: A Recurrent Neural Network Based Alternative to Convolutional NetworksCode0
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch RecognitionCode0
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable NetworksCode0
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural NetworkCode0
Generalizing to unseen domains via distribution matchingCode0
Deep Reconstruction-Classification Networks for Unsupervised Domain AdaptationCode0
Deep Predictive Coding Network with Local Recurrent Processing for Object RecognitionCode0
Deep Metric Transfer for Label Propagation with Limited Annotated DataCode0
Training Deep Neural Networks via Branch-and-BoundCode0
Texture Synthesis Using Convolutional Neural NetworksCode0
Residual Attention Network for Image ClassificationCode0
On the role of feedback in visual processing: a predictive coding perspectiveCode0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
A Dataset and Framework for Learning State-invariant Object RepresentationsCode0
Learning the Precise Feature for Cluster AssignmentCode0
Sparse 3D convolutional neural networksCode0
On zero-shot recognition of generic objectsCode0
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People DetectionCode0
Learning to Find Common Objects Across Few Image CollectionsCode0
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest ClassificationCode0
Opening Deep Neural Networks with Generative ModelsCode0
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep LearningCode0
OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair NavigationCode0
Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIMCode0
Are Vision Transformers More Data Hungry Than Newborn Visual Systems?Code0
Deep Learning with Nonparametric ClusteringCode0
Theano-based Large-Scale Visual Recognition with Multiple GPUsCode0
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training AcceleratorsCode0
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNsCode0
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech RecognitionCode0
Sparse Transfer Learning via Winning Lottery TicketsCode0
Deep Learning and Its Applications to Machine Health Monitoring: A SurveyCode0
Comparative evaluation of CNN architectures for Image Caption GenerationCode0
Learning Where to Edit Vision TransformersCode0
A Classification approach towards Unsupervised Learning of Visual RepresentationsCode0
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image ClassificationCode0
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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