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 1501–1550 of 2042 papers

TitleStatusHype
Image classification using local tensor singular value decompositions—0
A New Urban Objects Detection Framework Using Weakly Annotated Sets—0
Controlled Tactile Exploration and Haptic Object Recognition—0
Do Deep Neural Networks Suffer from Crowding?Code0
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition—0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO—0
Comparing deep neural networks against humans: object recognition when the signal gets weakerCode0
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
Analysis of dropout learning regarded as ensemble learning—0
Human-like Clustering with Deep Convolutional Neural NetworksCode0
Teaching Compositionality to CNNs—0
Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition—0
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks—0
CortexNet: a Generic Network Family for Robust Visual Temporal Representations—0
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation—0
A Random-Fern based Feature Approach for Image Matching—0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery—0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
Reflection Invariant and Symmetry Detection—0
Multi-View Task-Driven Recognition in Visual Sensor Networks—0
First-spike based visual categorization using reward-modulated STDP—0
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models—0
Learning Robust Object Recognition Using Composed Scenes from Generative Models—0
Forecasting Hands and Objects in Future Frames—0
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?—0
Learning Hard Alignments with Variational Inference—0
View-Invariant Template Matching Using Homography Constraints—0
Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities—0
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks—0
Reducing Bias in Production Speech Models—0
Collaborative Descriptors: Convolutional Maps for Preprocessing—0
CORe50: a New Dataset and Benchmark for Continuous Object RecognitionCode0
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition—0
DeepCorrect: Correcting DNN models against Image DistortionsCode0
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis—0
Recurrent Soft Attention Model for Common Object RecognitionCode0
Inception Recurrent Convolutional Neural Network for Object RecognitionCode0
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition—0
Residual Attention Network for Image ClassificationCode0
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition—0
Invariant recognition drives neural representations of action sequences—0
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections—0
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality—0
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image ClassificationCode0
Deep Affordance-grounded Sensorimotor Object Recognition—0
Generate To Adapt: Aligning Domains using Generative Adversarial NetworksCode0
Pragmatic descriptions of perceptual stimuli—0
Multiple Instance Detection Network with Online Instance Classifier RefinementCode1
Learning with Privileged Information for Multi-Label Classification—0
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer—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