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
Hierarchical Novelty Detection for Visual Object Recognition—0
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition—0
Exploiting Temporal Relations on Radar Perception for Autonomous Driving—0
Hierarchical Prototype Learning for Zero-Shot Recognition—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition—0
Highlight Timestamp Detection Model for Comedy Videos via Multimodal Sentiment Analysis—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation—0
How Can CNNs Use Image Position for Segmentation?—0
How Deep is the Feature Analysis underlying Rapid Visual Categorization?—0
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses—0
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision—0
How good are deep models in understanding the generated images?—0
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time—0
A Random-Fern based Feature Approach for Image Matching—0
How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning—0
How to deal with glare for improved perception of Autonomous Vehicles—0
A Framework For Refining Text Classification and Object Recognition from Academic Articles—0
How well do deep neural networks trained on object recognition characterize the mouse visual system?—0
A concatenating framework of shortcut convolutional neural networks—0
HR-RCNN: Hierarchical Relational Reasoning for Object Detection—0
3D Object Recognition with Deep Belief Nets—0
Exploiting an Oracle that Reports AUC Scores in Machine Learning Contests—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Exploit Bounding Box Annotations for Multi-label Object Recognition—0
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness—0
Human peripheral blur is optimal for object recognition—0
Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images—0
Humans and deep networks largely agree on which kinds of variation make object recognition harder—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
Human vs. Computer in Scene and Object Recognition—0
Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection—0
Hybrid Predictive Coding: Inferring, Fast and Slow—0
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Hyper-parameter optimization of Deep Convolutional Networks for object recognition—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
A Framework for Multi-View Classification of Features—0
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding—0
Identifying Object States in Cooking-Related Images—0
Identity documents recognition and detection using semantic segmentation with convolutional neural network—0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal—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
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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