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 576–600 of 2042 papers

TitleStatusHype
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