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 1351–1400 of 2042 papers

TitleStatusHype
Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots—0
Semantically Meaningful View SelectionCode0
Attention Mechanisms for Object Recognition with Event-Based Cameras—0
Human peripheral blur is optimal for object recognition—0
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech RecognitionCode0
End-to-End Race Driving with Deep Reinforcement Learning—0
Kitting in the Wild through Online Domain Adaptation—0
`Lighter' Can Still Be Dark: Modeling Comparative Color Descriptions—0
Estimating Bicycle Route Attractivity from Image Data—0
Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation—0
A temporal neural network model for object recognition using a biologically plausible decision making layer—0
Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter—0
Deep Global-Connected Net With The Generalized Multi-Piecewise ReLU Activation in Deep Learning—0
The Toybox Dataset of Egocentric Visual Object Transformations—0
NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage—0
Convex Class Model on Symmetric Positive Definite Manifolds—0
Delta-encoder: an effective sample synthesis method for few-shot object recognitionCode0
Model-based active learning to detect isometric deformable objects in the wild with deep architectures—0
Recurrent Convolutional Fusion for RGB-D Object RecognitionCode0
State Classification with CNN—0
A Classification approach towards Unsupervised Learning of Visual RepresentationsCode0
Multi-View Harmonized Bilinear Network for 3D Object Recognition—0
OLÉ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep LearningCode0
Geometry Aware Constrained Optimization Techniques for Deep Learning—0
Duplex Generative Adversarial Network for Unsupervised Domain Adaptation—0
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash—0
Multi-level 3D CNN for Learning Multi-scale Spatial FeaturesCode0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-OrganizationCode0
Deep Watershed Detector for Music Object Recognition—0
Learning Illuminant Estimation from Object Recognition—0
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching—0
Deformable Part Networks—0
Object Localization with a Weakly Supervised CapsNet—0
Deep Predictive Coding Network with Local Recurrent Processing for Object RecognitionCode0
Disparity Sliding Window: Object Proposals From Disparity ImagesCode0
Identifying Object States in Cooking-Related Images—0
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation—0
Energy Efficient Hadamard Neural Networks—0
Dense and Diverse Capsule Networks: Making the Capsules Learn BetterCode0
SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks—0
Object and Text-guided Semantics for CNN-based Activity Recognition—0
SdcNet: A Computation-Efficient CNN for Object Recognition—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
Incorporating Semantic Attention in Video Description Generation—0
Semi-supervised Training Data Generation for Multilingual Question Answering—0
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving ObjectsCode0
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism—0
Semantic Edge Detection with Diverse Deep SupervisionCode0
Performance Evaluation of 3D Correspondence Grouping Algorithms—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