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 951–1000 of 2042 papers

TitleStatusHype
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data—0
Transformers: State-of-the-Art Natural Language ProcessingCode0
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision—0
Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs—0
Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation—0
PennSyn2Real: Training Object Recognition Models without Human Labeling—0
Angular Luminance for Material Segmentation—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable NetworksCode0
A Multisensory Learning Architecture for Rotation-invariant Object Recognition—0
Object Recognition for Economic Development from Daytime Satellite Imagery—0
Performance of object recognition in wearable videos—0
SWP-LeafNET: A novel multistage approach for plant leaf identification based on deep CNN—0
Binarized Neural Architecture Search for Efficient Object Recognition—0
A Review on Near Duplicate Detection of Images using Computer Vision Techniques—0
Privacy Leakage of SIFT Features via Deep Generative Model based Image ReconstructionCode0
Deep Learning Techniques for Geospatial Data Analysis—0
Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature—0
All About Knowledge Graphs for Actions—0
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds—0
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label ImbalanceCode0
Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways—0
Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based DetectionCode0
A Self-supervised GAN for Unsupervised Few-shot Object Recognition—0
PAM:Point-wise Attention Module for 6D Object Pose Estimation—0
Adversarial Examples on Object Recognition: A Comprehensive Survey—0
Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning—0
Active Perception using Light Curtains for Autonomous Driving—0
More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition—0
MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos—0
Multiple Class Novelty Detection Under Data Distribution Shift—0
Self-supervised Visual Attribute Learning for Fashion Compatibility—0
Feature Learning for Accelerometer based Gait RecognitionCode0
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases—0
Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve—0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains—0
Self-Supervised Learning Across Domains—0
Parkinson's Disease Detection with Ensemble Architectures based on ILSVRC Models—0
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization—0
Adaptive Hierarchical Decomposition of Large Deep Networks—0
The Effect of Top-Down Attention in Occluded Object Recognition—0
Nested Learning For Multi-Granular Tasks—0
Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation—0
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation—0
Quaternion Capsule NetworksCode0
Are Labels Always Necessary for Classifier Accuracy Evaluation?—0
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks—0
Parkinson's Disease Detection Using Ensemble Architecture from MR Images—0
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistencyCode0
Fast Training of Deep Networks with One-Class CNNs—0
Show:102550
← PrevPage 20 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