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 2001–2042 of 2042 papers

TitleStatusHype
Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities—0
Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching—0
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users—0
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data—0
Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects—0
A Voxel Graph CNN for Object Classification with Event Cameras—0
Exact neural mass model for synaptic-based working memory—0
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment—0
Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness—0
Exploit Bounding Box Annotations for Multi-label Object Recognition—0
Exploiting an Oracle that Reports AUC Scores in Machine Learning Contests—0
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
Exploiting Temporal Relations on Radar Perception for Autonomous Driving—0
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
Exploration of object recognition from 3D point cloud—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
Exponential Discriminative Metric Embedding in Deep Learning—0
Extreme Image Transformations Affect Humans and Machines Differently—0
Extreme Image Transformations Facilitate Robust Latent Object Representations—0
EZSR: Event-based Zero-Shot Recognition—0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
Face Identification with Second-Order Pooling—0
Face processing emerges from object-trained convolutional neural networks—0
Face-space Action Recognition by Face-Object Interactions—0
Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation—0
Farm land weed detection with region-based deep convolutional neural networks—0
Fashioning with Networks: Neural Style Transfer to Design Clothes—0
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition—0
Fast Deep Predictive Coding Networks for Videos Feature Extraction without Labels—0
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations—0
Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition—0
Fast Neuromimetic Object Recognition using FPGA Outperforms GPU Implementations—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