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 551–600 of 2042 papers

TitleStatusHype
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition—0
A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations—0
Categorical Mixture Models on VGGNet activations—0
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity—0
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures—0
Efficient Point-to-Subspace Query in ^1 with Application to Robust Object Instance Recognition—0
EMPIRICAL UPPER BOUND IN OBJECT DETECTION—0
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks—0
Cascade Region Proposal and Global Context for Deep Object Detection—0
An optical biomimetic eyes with interested object imaging—0
A Non-Technical Survey on Deep Convolutional Neural Network Architectures—0
Capturing the objects of vision with neural networks—0
Adversarial Examples on Object Recognition: A Comprehensive Survey—0
Edge Aware Learning for 3D Point Cloud—0
An online passive-aggressive algorithm for difference-of-squares classification—0
Capacity limitations of visual search in deep convolutional neural networks—0
A comparable study: Intrinsic difficulties of practical plant diagnosis from wide-angle images—0
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection—0
An online passive-aggressive algorithm for difference-of-squares classification—0
Adversarial Detection by Approximation of Ensemble Boundary—0
ECOR: Explainable CLIP for Object Recognition—0
Edge Detection Based Shape Identification—0
Anomaly Detection with Domain Adaptation—0
Can foundation models actively gather information in interactive environments to test hypotheses?—0
Adversarial Attacks and Defense on Texts: A Survey—0
Can domain adaptation make object recognition work for everyone?—0
Can Boosting with SVM as Week Learners Help?—0
Annotation of Online Shopping Images without Labeled Training Examples—0
1 Million Captioned Dutch Newspaper Images—0
CAggNet: Crossing Aggregation Network for Medical Image Segmentation—0
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing—0
Angular Luminance for Material Segmentation—0
A New Urban Objects Detection Framework Using Weakly Annotated Sets—0
Adversarial Attack on Facial Recognition using Visible Light—0
A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations—0
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference—0
EdgeOL: Efficient in-situ Online Learning on Edge Devices—0
Building Machines That Learn and Think Like People—0
Building a visual semantics aware object hierarchy—0
A New Manifold Distance Measure for Visual Object Categorization—0
BSED: Baseline Shapley-Based Explainable Detector—0
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture—0
A new GAN-based anomaly detection (GBAD) approach for multi-threat object classification on large-scale x-ray security images—0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception—0
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields—0
A newborn embodied Turing test for view-invariant 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