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 1501–1525 of 2042 papers

TitleStatusHype
Achieving Competitive Play Through Bottom-Up Approach in Semantic Segmentation—0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured—0
A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations—0
A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images—0
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis—0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification—0
A Comprehensive Review of Modern Object Segmentation Approaches—0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
A concatenating framework of shortcut convolutional neural networks—0
A Convolutional Neural Network based Live Object Recognition System as Blind Aid—0
Active Gaze Behavior Boosts Self-Supervised Object Learning—0
Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots—0
Active Perception using Light Curtains for Autonomous Driving—0
Adaptation Across Extreme Variations using Unlabeled Domain Bridges—0
Adaptive Active Learning for Image Classification—0
Adaptive Color Attributes for Real-Time Visual Tracking—0
Fractional order graph neural network—0
Adaptive Hierarchical Decomposition of Large Deep Networks—0
Adaptive Object Detection with Dual Multi-Label Prediction—0
A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application—0
ADD-SLAM: Adaptive Dynamic Dense SLAM with Gaussian Splatting—0
A Deeper Look at the Unsupervised Learning of Disentangled Representations in β-VAE from the Perspective of Core Object Recognition—0
A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distance—0
A Discriminative Representation of Convolutional Features for Indoor Scene 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