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 1526–1550 of 2042 papers

TitleStatusHype
A Discriminative Vectorial Framework for Multi-modal Feature Representation—0
ADLDA: A Method to Reduce the Harm of Data Distribution Shift in Data Augmentation—0
A Dual-hierarchy Semantic Graph for Robust Object Recognition—0
Advancing Egocentric Video Question Answering with Multimodal Large Language Models—0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception—0
Adversarial Attack on Facial Recognition using Visible Light—0
Adversarial Attacks and Defense on Texts: A Survey—0
Adversarial Detection by Approximation of Ensemble Boundary—0
Adversarial Examples on Object Recognition: A Comprehensive Survey—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition—0
ADVISE: Symbolism and External Knowledge for Decoding Advertisements—0
A Dynamic Programming Approach for Fast and Robust Object Pose Recognition From Range Images—0
A dynamic vision sensor object recognition model based on trainable event-driven convolution and spiking attention mechanism—0
A Feature Learning and Object Recognition Framework for Underwater Fish Images—0
Affordance Labeling and Exploration: A Manifold-Based Approach—0
Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation—0
A Fog Robotic System for Dynamic Visual Servoing—0
A Framework for Multi-View Classification of Features—0
A Framework For Refining Text Classification and Object Recognition from Academic Articles—0
A Generic Regression Framework for Pose Recognition on Color and Depth Images—0
Agricultural Object Detection with You Look Only Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review—0
A hierarchical framework for object recognition—0
A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing—0
AI-based Density Recognition—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