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 1676–1700 of 2042 papers

TitleStatusHype
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture—0
Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration—0
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM—0
Background Invariance Testing According to Semantic Proximity—0
Bandwidth limited object recognition in high resolution imagery—0
Basic Level Categorization Facilitates Visual Object Recognition—0
Belief Tree Search for Active Object Recognition—0
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex—0
Benchmarking VLMs' Reasoning About Persuasive Atypical Images—0
Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images—0
Better Exploiting OS-CNNs for Better Event Recognition in Images—0
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States—0
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree—0
Beyond Object Recognition: A New Benchmark towards Object Concept Learning—0
Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models—0
Beyond Semantics: Rediscovering Spatial Awareness in Vision-Language Models—0
Beyond Sharing Weights for Deep Domain Adaptation—0
Beyond Spatial Pooling: Fine-Grained Representation Learning in Multiple Domains—0
Beyond Viewpoint: Robust 3D Object Recognition under Arbitrary Views through Joint Multi-Part Representation—0
GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition—0
BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification—0
big.LITTLE Vision Transformer for Efficient Visual Recognition—0
BIM-assisted object recognition for the on-site autonomous robotic assembly of discrete structures—0
BIM Hyperreality: Data Synthesis Using BIM and Hyperrealistic Rendering for Deep Learning—0
Binarized Neural Architecture Search for Efficient 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