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 451–500 of 2042 papers

TitleStatusHype
BViT: Broad Attention based Vision TransformerCode0
PydMobileNet: Improved Version of MobileNets with Pyramid Depthwise Separable ConvolutionCode0
Quaternion Capsule NetworksCode0
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable NetworksCode0
Real-Time Correlation Tracking via Joint Model Compression and TransferCode0
Feature Pyramid GridsCode0
Recent Advances in Neural Program SynthesisCode0
Food Image Recognition by Using Convolutional Neural Networks (CNNs)Code0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Exploring Novel Object Recognition and Spontaneous Location Recognition Machine Learning Analysis Techniques in Alzheimer's MiceCode0
DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionCode0
Decision-making and control with diffractive optical networksCode0
Facial Expression Recognition Research Based on Deep LearningCode0
EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving ObjectCode0
A Classification approach towards Unsupervised Learning of Visual RepresentationsCode0
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural NetworkCode0
Experiments with mmWave Automotive Radar Test-bedCode0
Robust Graph Learning from Noisy DataCode0
Training Deep Neural Networks via Branch-and-BoundCode0
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training DataCode0
Ensemble learning in CNN augmented with fully connected subnetworksCode0
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNsCode0
Sample Correlation for Fingerprinting Deep Face RecognitionCode0
Deep Co-Occurrence Feature Learning for Visual Object RecognitionCode0
DeepCorrect: Correcting DNN models against Image DistortionsCode0
Deep Cross Residual Learning for Multitask Visual RecognitionCode0
Deep Discrete Hashing with Self-supervised Pairwise LabelsCode0
Deep Feature Collaboration for Challenging 3D Finger Knuckle IdentificationCode0
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the WildCode0
Optimizing Spatio-Temporal Information Processing in Spiking Neural Networks via Unconstrained Leaky Integrate-and-Fire Neurons and Hybrid CodingCode0
Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object RecognitionCode0
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch RecognitionCode0
A Domain Guided CNN Architecture for Predicting Age from Structural Brain ImagesCode0
End-to-End Learning of Representations for Asynchronous Event-Based DataCode0
Faster gaze prediction with dense networks and Fisher pruningCode0
Generalized Relevance Learning Grassmann QuantizationCode0
Dynamic Rectification Knowledge DistillationCode0
Do Pre-trained Vision-Language Models Encode Object States?Code0
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
Seq-NMS for Video Object DetectionCode0
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training AcceleratorsCode0
Domain Generalization via Model-Agnostic Learning of Semantic FeaturesCode0
Domain Generalization In Robust Invariant RepresentationCode0
Dominant Set Clustering and Pooling for Multi-View 3D Object RecognitionCode0
Domain Generalization by Solving Jigsaw PuzzlesCode0
Domain Generalization by Solving Jigsaw PuzzlesCode0
Don't Judge by the Look: Towards Motion Coherent Video RepresentationCode0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
Improving Out-of-Distribution Detection with Disentangled Foreground and Background FeaturesCode0
Do Deep Neural Networks Suffer from Crowding?Code0
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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