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 551–600 of 2042 papers

TitleStatusHype
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
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configurationCode0
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth EstimationCode0
Domain-aware Triplet loss in Domain GeneralizationCode0
Don't Judge by the Look: Towards Motion Coherent Video RepresentationCode0
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch RecognitionCode0
Geometric and Textural Augmentation for Domain Gap ReductionCode0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Image Privacy Prediction Using Deep Neural NetworksCode0
Local Aggregation for Unsupervised Learning of Visual EmbeddingsCode0
Grid Cell Path Integration For Movement-Based Visual Object RecognitionCode0
Pointwise Convolutional Neural NetworksCode0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language ModelsCode0
Hierarchical Superpixel Segmentation via Structural Information TheoryCode0
Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition—0
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations—0
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex—0
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition—0
Deep Network Guided Proof Search—0
Belief Tree Search for Active Object Recognition—0
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation—0
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers—0
Deep Models for Multi-View 3D Object Recognition: A Review—0
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition—0
Basic Level Categorization Facilitates Visual Object Recognition—0
An Adaptive Descriptor Design for Object Recognition in the Wild—0
Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems—0
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer—0
Deep Learning with Logged Bandit Feedback—0
Bandwidth limited object recognition in high resolution imagery—0
Deep Learning with Energy-efficient Binary Gradient Cameras—0
Deep Learning Techniques for Geospatial Data Analysis—0
Background Invariance Testing According to Semantic Proximity—0
Deep learning systems as complex networks—0
Deep Learning Object Detection Methods for Ecological Camera Trap Data—0
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM—0
A Multisensory Learning Architecture for Rotation-invariant Object Recognition—0
A biologically plausible network for the computation of orientation dominance—0
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition—0
Deep Learning for the Classification of Lung Nodules—0
Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration—0
Deep Learning for Material recognition: most recent advances and open challenges—0
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture—0
A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth—0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery—0
Deep learning based infrared small object segmentation: Challenges and future directions—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