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 501–550 of 2042 papers

TitleStatusHype
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer—0
Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems—0
Basic Level Categorization Facilitates Visual Object Recognition—0
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition—0
Deep Models for Multi-View 3D Object Recognition: A Review—0
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers—0
Deep Network Guided Proof Search—0
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition—0
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations—0
Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition—0
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
A Poodle or a Dog? Evaluating Automatic Image Annotation Using Human Descriptions at Different Levels of Granularity—0
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks—0
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—0
A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching—0
A Comprehensive Review of Modern Object Segmentation Approaches—0
Diversity in Object Proposals—0
Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images—0
Anytime Recognition of Objects and Scenes—0
Class incremental learning for video action classification—0
Classifying Malware Images with Convolutional Neural Network Models—0
A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanism—0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification—0
Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier—0
Answer-Type Prediction for Visual Question Answering—0
A Dynamic Programming Approach for Fast and Robust Object Pose Recognition From Range Images—0
Classification and Geometry of General Perceptual Manifolds—0
CIFAR10 to Compare Visual Recognition Performance between Deep Neural Networks and Humans—0
An Overview Of 3D Object Detection—0
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN—0
DNN Quantization with Attention—0
Chosen methods of improving small object recognition with weak recognizable features—0
ChoiceNet: CNN learning through choice of multiple feature map representations—0
A Novel mapping for visual to auditory sensory substitution—0
A Novel Locally Linear KNN Model for Visual Recognition—0
ChartKG: A Knowledge-Graph-Based Representation for Chart Images—0
ADVISE: Symbolism and External Knowledge for Decoding Advertisements—0
Disentangling Properties of Contrastive Methods—0
A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations—0
Characterising representation dynamics in recurrent neural networks for object recognition—0
VGG Fine-tuning for Cooking State Recognition—0
Certifiable Artificial Intelligence Through Data Fusion—0
A Novel Feature Extraction Method for Scene Recognition Based on Centered Convolutional Restricted Boltzmann Machines—0
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
Distributed Coding of Multiview Sparse Sources with Joint Recovery—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
A Novel Explainable Artificial Intelligence Model in Image Classification problem—0
Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition—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