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 1551–1575 of 2042 papers

TitleStatusHype
AI-based Wearable Vision Assistance System for the Visually Impaired: Integrating Real-Time Object Recognition and Contextual Understanding Using Large Vision-Language Models—0
AI-Powered Assistive Technologies for Visual Impairment—0
AI-Powered Augmented Reality for Satellite Assembly, Integration and Test—0
AI-Powered GUI Attack and Its Defensive Methods—0
AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method—0
AIris: An AI-powered Wearable Assistive Device for the Visually Impaired—0
Airplane Type Identification Based on Mask RCNN; An Approach to Reduce Airport Traffic Congestion—0
A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition—0
A large comparison of feature-based approaches for buried target classification in forward-looking ground-penetrating radar—0
ALGO: Object-Grounded Visual Commonsense Reasoning for Open-World Egocentric Action Recognition—0
Algorithms for Object Detection in Substations—0
`Lighter' Can Still Be Dark: Modeling Comparative Color Descriptions—0
Aligning Text, Images, and 3D Structure Token-by-Token—0
All About Knowledge Graphs for Actions—0
All-Weather Object Recognition Using Radar and Infrared Sensing—0
A low-power end-to-end hybrid neuromorphic framework for surveillance applications—0
A Machine Learning Framework for Distributed Functional Compression over Wireless Channels in IoT—0
Amodal Completion and Size Constancy in Natural Scenes—0
Amplitude-Based Approach to Evidence Accumulation—0
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness—0
A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth—0
A Multisensory Learning Architecture for Rotation-invariant Object Recognition—0
An Adaptive Descriptor Design for Object Recognition in the Wild—0
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation—0
An Additive Latent Feature Model for Transparent Object 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