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 551–600 of 2042 papers

TitleStatusHype
Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies—0
A priori compression of convolutional neural networks for wave simulators—0
Pinpointing Why Object Recognition Performance Degrades Across Income Levels and GeographiesCode0
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations—0
Domain Generalization In Robust Invariant RepresentationCode0
What's in a Name? Beyond Class Indices for Image Recognition—0
Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection—0
Improving Out-of-Distribution Detection with Disentangled Foreground and Background FeaturesCode0
Feature representations useful for predicting image memorability—0
Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California—0
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
Toward a Geometric Theory of Manifold Untangling—0
Domain-aware Triplet loss in Domain GeneralizationCode0
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving—0
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation—0
Scaling Vision Transformers to 22 Billion ParametersCode0
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery—0
Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects—0
Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation—0
Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object ClassificationCode0
Connecting metrics for shape-texture knowledge in computer vision—0
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation—0
Effective Baselines for Multiple Object Rearrangement Planning in Partially Observable Mapped Environments—0
ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition—0
Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans—0
A Comprehensive Review of Modern Object Segmentation Approaches—0
Visual Story Generation Based on Emotion and KeywordsCode0
GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition—0
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition—0
Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration—0
Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition—0
MISC210K: A Large-Scale Dataset for Multi-Instance Semantic CorrespondenceCode0
Parsing Objects at a Finer Granularity: A Survey—0
Brain Cancer Segmentation Using YOLOv5 Deep Neural Network—0
Decision-making and control with diffractive optical networksCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
ColorSense: A Study on Color Vision in Machine Visual Recognition—0
OAMixer: Object-aware Mixing Layer for Vision TransformersCode0
A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations—0
Teaching What You Should Teach: A Data-Based Distillation Method—0
State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions—0
Beyond Object Recognition: A New Benchmark towards Object Concept Learning—0
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural NetworksCode0
Extreme Image Transformations Affect Humans and Machines Differently—0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—0
Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition—0
Metric Learning as a Service with Covariance Embedding—0
Learning to Learn: How to Continuously Teach Humans and Machines—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