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
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness—0
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations—0
Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition—0
Image Optimization and Prediction—0
A Framework for Multi-View Classification of Features—0
Combined Approach for Image Segmentation—0
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment—0
Combinatorial clustering and the beta negative binomial process—0
Imaging-free object recognition enabled by optical coherence—0
Immersive Language Exploration with Object Recognition and Augmented Reality—0
A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method—0
I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data—0
Improved Deep Learning of Object Category using Pose Information—0
Improved Deep Metric Learning with Multi-class N-pair Loss Objective—0
Exact neural mass model for synaptic-based working memory—0
Improved Inception-Residual Convolutional Neural Network for Object Recognition—0
Improved training of binary networks for human pose estimation and image recognition—0
Improved visible to IR image transformation using synthetic data augmentation with cycle-consistent adversarial networks—0
A Voxel Graph CNN for Object Classification with Event Cameras—0
Combinational neural network using Gabor filters for the classification of handwritten digits—0
Coloring Objects: Adjective-Noun Visual Semantic Compositionality—0
A priori compression of convolutional neural networks for wave simulators—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Improving Gibbs Sampler Scan Quality with DoGS—0
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations—0
A Fog Robotic System for Dynamic Visual Servoing—0
Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study—0
Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans—0
EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs—0
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer—0
Collaborative Descriptors: Convolutional Maps for Preprocessing—0
Approximation of dilation-based spatial relations to add structural constraints in neural networks—0
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects—0
EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation—0
Incorporating Semantic Attention in Video Description Generation—0
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection—0
Incorporating Textual Evidence in Visual Storytelling—0
Incremental Learning for Robot Perception through HRI—0
Collaboration Analysis Using Deep Learning—0
Inducing Functions through Reinforcement Learning without Task Specification—0
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification—0
Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation—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