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

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Showing 1751–1800 of 2042 papers

TitleStatusHype
Sample-Efficient Learning of Novel Visual ConceptsCode0
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point CloudsCode0
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networksCode0
Scalable Bayesian Optimization Using Deep Neural NetworksCode0
Scalable Object Detection using Deep Neural NetworksCode0
An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation DatasetCode0
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral StreamCode0
CNN-based Methods for Object Recognition with High-Resolution Tactile SensorsCode0
STDP-based spiking deep convolutional neural networks for object recognitionCode0
Scaling Vision Transformers to 22 Billion ParametersCode0
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional NetworksCode0
Hierarchical Superpixel Segmentation via Structural Information TheoryCode0
Persistent Homology Meets Object Unity: Object Recognition in ClutterCode0
Weakly-supervised DCNN for RGB-D Object Recognition in Real-World Applications Which Lack Large-scale Annotated Training DataCode0
CORe50: a New Dataset and Benchmark for Continuous Object RecognitionCode0
Striving for Simplicity: The All Convolutional NetCode0
Distinctive Image Features from Scale-Invariant KeypointsCode0
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural networkCode0
Pinpointing Why Object Recognition Performance Degrades Across Income Levels and GeographiesCode0
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the WildCode0
AGA: Attribute Guided AugmentationCode0
Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated InteractionsCode0
How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition?Code0
Scene Recognition by Combining Local and Global Image DescriptorsCode0
Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic InformationCode0
Sublinear Partition EstimationCode0
UnSegGNet: Unsupervised Image Segmentation using Graph Neural NetworksCode0
Places205-VGGNet Models for Scene RecognitionCode0
Multi-area Target Individual Detection with Free Drawing on VideoCode0
A Framework of Transfer Learning in Object Detection for Embedded SystemsCode0
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial NoisesCode0
Point Cloud GANCode0
Human-like Clustering with Deep Convolutional Neural NetworksCode0
An Analysis of Unsupervised Pre-training in Light of Recent AdvancesCode0
Chessboard and chess piece recognition with the support of neural networksCode0
Human Pose Estimation for Real-World Crowded ScenariosCode0
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label ImbalanceCode0
A comparison between humans and AI at recognizing objects in unusual posesCode0
Disparity Sliding Window: Object Proposals From Disparity ImagesCode0
Pointwise Convolutional Neural NetworksCode0
Multimodal Deep Learning for Robust RGB-D Object RecognitionCode0
Five Points to Check when Comparing Visual Perception in Humans and MachinesCode0
Multi-path Convolutional Neural Networks for Complex Image ClassificationCode0
Foveated Instance SegmentationCode0
Continual Learning in Neural NetworksCode0
Tuned Compositional Feature Replays for Efficient Stream LearningCode0
Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksCode0
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural NetworksCode0
Visual Story Generation Based on Emotion and KeywordsCode0
Improving Out-of-Distribution Detection with Disentangled Foreground and Background FeaturesCode0
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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