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 901–950 of 2042 papers

TitleStatusHype
Hierarchical Novelty Detection for Visual Object Recognition—0
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition—0
Hierarchical Piecewise-Constant Super-regions—0
Hierarchical Prototype Learning for Zero-Shot Recognition—0
Data-driven Verification of DNNs for Object Recognition—0
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition—0
Highlight Timestamp Detection Model for Comedy Videos via Multimodal Sentiment Analysis—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets—0
How Can CNNs Use Image Position for Segmentation?—0
How Deep is the Feature Analysis underlying Rapid Visual Categorization?—0
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses—0
Discrete Potts Model for Generating Superpixels on Noisy Images—0
How good are deep models in understanding the generated images?—0
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time—0
DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell Classification with LIME-Based Explainability—0
How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning—0
How to deal with glare for improved perception of Autonomous Vehicles—0
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes—0
How well do deep neural networks trained on object recognition characterize the mouse visual system?—0
Brain Cancer Segmentation Using YOLOv5 Deep Neural Network—0
HR-RCNN: Hierarchical Relational Reasoning for Object Detection—0
Human Action Recognition in Still Images Using ConViT—0
Fractional order graph neural network—0
Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments—0
MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence—0
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation—0
Human peripheral blur is optimal for object recognition—0
Deep Active Object Recognition by Joint Label and Action Prediction—0
Humans and deep networks largely agree on which kinds of variation make object recognition harder—0
Deep Affordance-grounded Sensorimotor Object Recognition—0
Human vs. Computer in Scene and Object Recognition—0
Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection—0
Hybrid Predictive Coding: Inferring, Fast and Slow—0
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data—0
Hyper-parameter optimization of Deep Convolutional Networks for object recognition—0
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations—0
Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition—0
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning—0
iCub World: Friendly Robots Help Building Good Vision Data-Sets—0
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding—0
Identifying Object States in Cooking-Related Images—0
Identity documents recognition and detection using semantic segmentation with convolutional neural network—0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal—0
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review—0
Image Captioning with Unseen Objects—0
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning—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