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 801–850 of 2042 papers

TitleStatusHype
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations—0
Energy-based Dropout in Restricted Boltzmann Machines: Why not go random—0
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices—0
Self-Supervised Pretraining of 3D Features on any Point-CloudCode1
Look Twice: A Generalist Computational Model Predicts Return Fixations across Tasks and SpeciesCode0
Unity of Opposites: SelfNorm and CrossNorm for Model Robustness—0
CONTEMPLATING REAL-WORLDOBJECT RECOGNITION—0
EMPIRICAL UPPER BOUND IN OBJECT DETECTION—0
Sample Balancing for Improving Generalization under Distribution Shifts—0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods.—0
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation—0
On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations—0
On the Robustness of Sentiment Analysis for Stock Price Forecasting—0
Modeling Human Development: Effects of Blurred Vision on Category Learning in CNNs—0
Learning Semantic Similarities for Prototypical Classifiers—0
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral StreamCode0
Visual Probing and Correction of Object Recognition Models with Interactive user feedbackCode0
Adaptive Threshold for Online Object Recognition and Re-identification TasksCode1
Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception—0
Projected Distribution Loss for Image EnhancementCode0
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets—0
Deep Learning for Material recognition: most recent advances and open challenges—0
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling TransferCode1
Assessing The Importance Of Colours For CNNs In Object Recognition—0
Full-Glow: Fully conditional Glow for more realistic image generationCode1
The Lottery Ticket Hypothesis for Object RecognitionCode1
Interpretable Graph Capsule Networks for Object Recognition—0
Unsupervised Part Discovery via Feature Alignment—0
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image PerturbationsCode1
Towards real-time object recognition and pose estimation in point clouds—0
Sparse R-CNN: End-to-End Object Detection with Learnable ProposalsCode2
Adversarial Attack on Facial Recognition using Visible Light—0
Insights From A Large-Scale Database of Material Depictions In Paintings—0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19—0
Complex-valued Iris Recognition Network—0
Enriching ImageNet with Human Similarity Judgments and Psychological EmbeddingsCode1
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations—0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer—0
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object RecognitionCode1
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection—0
I-POST: Intelligent Point of Sale and Transaction System—0
Adding Knowledge to Unsupervised Algorithms for the Recognition of IntentCode0
Transferred Fusion Learning using Skipped Networks—0
Out-of-Distribution Detection for Automotive Perception—0
On Numerosity of Deep Neural Networks—0
A Study of Image Pre-processing for Faster Object Recognition—0
On the Performance of Convolutional Neural Networks under High and Low Frequency Information—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