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 651–675 of 2042 papers

TitleStatusHype
Data-driven Verification of DNNs for Object Recognition—0
Data-Driven 3D Voxel Patterns for Object Category Recognition—0
A temporal neural network model for object recognition using a biologically plausible decision making layer—0
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes—0
DAS: A Deformable Attention to Capture Salient Information in CNNs—0
A Systematic Study on Object Recognition Using Millimeter-wave Radar—0
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection—0
A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video—0
`Lighter' Can Still Be Dark: Modeling Comparative Color Descriptions—0
Adaptive Color Attributes for Real-Time Visual Tracking—0
AAD: Adaptive Anomaly Detection through traffic surveillance videos—0
MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence—0
CVSNet: A Computer Implementation for Central Visual System of The Brain—0
A Survey of Task-Based Machine Learning Content Extraction Services for VIDINT—0
CURL: Co-trained Unsupervised Representation Learning for Image Classification—0
A Study of Image Pre-processing for Faster Object Recognition—0
Algorithms for Object Detection in Substations—0
Crowdsourcing in Computer Vision—0
A Study for Universal Adversarial Attacks on Texture Recognition—0
ALGO: Object-Grounded Visual Commonsense Reasoning for Open-World Egocentric Action Recognition—0
Adaptive Active Learning for Image Classification—0
CPWC: Contextual Point Wise Convolution for Object Recognition—0
Counting the learnable functions of structured data—0
Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition—0
Co-training Transformer with Videos and Images Improves Action 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