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

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Showing 801–850 of 2042 papers

TitleStatusHype
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration—0
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations—0
Fast Deep Predictive Coding Networks for Videos Feature Extraction without Labels—0
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition—0
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion—0
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?—0
Forecasting Hands and Objects in Future Frames—0
Fashioning with Networks: Neural Style Transfer to Design Clothes—0
Farm land weed detection with region-based deep convolutional neural networks—0
Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition—0
Foveated Downsampling Techniques—0
Complex-valued Iris Recognition Network—0
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable—0
Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation—0
Face-space Action Recognition by Face-Object Interactions—0
Face processing emerges from object-trained convolutional neural networks—0
From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes—0
From Virtual to Real: A Framework for Verbal Interaction with Robots—0
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets—0
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis—0
A recurrent multi-scale approach to RBG-D Object Recognition—0
A Convolutional Neural Network based Live Object Recognition System as Blind Aid—0
Face Identification with Second-Order Pooling—0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
Fully-Connected CRFs with Non-Parametric Pairwise Potential—0
FusionNet: 3D Object Classification Using Multiple Data Representations—0
Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking—0
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study—0
EZSR: Event-based Zero-Shot Recognition—0
GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs—0
Extreme Image Transformations Facilitate Robust Latent Object Representations—0
A Real-time Junk Food Recognition System based on Machine Learning—0
Extreme Image Transformations Affect Humans and Machines Differently—0
Generalization Boosted Adapter for Open-Vocabulary Segmentation—0
Generalized Adaptive Dictionary Learning via Domain Shift Minimization—0
Generalized K-fan Multimodal Deep Model with Shared Representations—0
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition—0
Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval—0
Exponential Discriminative Metric Embedding in Deep Learning—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks—0
Generating Image Descriptions with Gold Standard Visual Inputs: Motivation, Evaluation and Baselines—0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models—0
Are Accuracy and Robustness Correlated?—0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing—0
Exploration of object recognition from 3D point cloud—0
A randomized gradient-free attack on ReLU networks—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