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
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
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance SegmentationCode1
Offline Meta-Reinforcement Learning with Advantage WeightingCode1
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
TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object RecognitionCode1
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
Do Adversarially Robust ImageNet Models Transfer Better?Code1
When and how CNNs generalize to out-of-distribution category-viewpoint combinationsCode1
Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation—0
Nested Learning For Multi-Granular Tasks—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
Single Shot MC Dropout ApproximationCode1
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
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion—0
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation ProcessCode0
Teaching CNNs to mimic Human Visual Cognitive Process & regularise Texture-Shape biasCode0
Hierarchically Compositional Tasks and Deep Convolutional Networks—0
Object recognition through pose and shape estimation—0
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation—0
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification—0
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group SoftmaxCode1
Enhancing Few-Shot Image Classification with Unlabelled Examples—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