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

Showing 10011050 of 2042 papers

TitleStatusHype
ObjectNet Dataset: Reanalysis and CorrectionCode1
Attribution in Scale and SpaceCode1
Randomized Kernel Multi-view Discriminant Analysis0
Look-into-Object: Self-supervised Structure Modeling for Object RecognitionCode1
Semantic Domain Adversarial Networks for Unsupervised Domain Adaptation0
Adaptive Object Detection with Dual Multi-Label Prediction0
Exploit Clues from Views: Self-Supervised and Regularized Learning for Multiview Object RecognitionCode1
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural networkCode0
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning modelsCode1
Event-based Asynchronous Sparse Convolutional NetworksCode1
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual CategorizationCode1
Ensemble learning in CNN augmented with fully connected subnetworksCode0
Pose Augmentation: Class-agnostic Object Pose Transformation for Object RecognitionCode1
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset0
Towards a Framework for Visual Intelligence in Service Robotics: Epistemic Requirements and Gap Analysis0
Equalization Loss for Long-Tailed Object RecognitionCode1
Measurement-driven Analysis of an Edge-Assisted Object Recognition System0
The iCub multisensor datasets for robot and computer vision applications0
Hand-Priming in Object Localization for Assistive Egocentric Vision0
Triangle-Net: Towards Robustness in Point Cloud LearningCode0
A Simple Framework for Contrastive Learning of Visual RepresentationsCode2
Compositional Embeddings for Multi-Label One-Shot Learning0
Self-Supervised Linear Motion DeblurringCode1
Investigating the Importance of Shape Features, Color Constancy, Color Spaces and Similarity Measures in Open-Ended 3D Object Recognition0
Variable-Viewpoint Representations for 3D Object Recognition0
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks0
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction0
Analyzing the Dependency of ConvNets on Spatial Information0
Selective Segmentation Networks Using Top-Down Attention0
EGO-CH: Dataset and Fundamental Tasks for Visitors BehavioralUnderstanding using Egocentric Vision0
OPFython: A Python-Inspired Optimum-Path Forest ClassifierCode1
AI-Powered GUI Attack and Its Defensive Methods0
Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving0
Deleting object selective units in a fully-connected layer of deep convolutional networks improves classification performance0
Block-wise Scrambled Image Recognition Using Adaptation Network0
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future0
A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration0
Learning Transformation-Aware Embeddings for Image Forensics0
Multi-Scale Weight Sharing Network for Image Recognition0
RMNv2: Reduced Mobilenet V2 for CIFAR100
Convolutional Networks with Dense Connectivity0
Fractional order graph neural network0
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?Code1
UW-NET: AN INCEPTION-ATTENTION NETWORK FOR UNDERWATER IMAGE CLASSIFICATION0
KNN-Based Automatic Cropping for Improved Threat Object Recognition in X-Ray Security Images0
Experiments with mmWave Automotive Radar Test-bedCode0
Application of Deep Learning in Generating Desired Design Options: Experiments Using Synthetic Training Dataset0
Importance Filtered Cross-Domain Adaptation0
Interactive Open-Ended Learning for 3D Object Recognition0
Deep-learning-based classification and retrieval of components of a process plant from segmented point clouds0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified