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 426–450 of 2042 papers

TitleStatusHype
Convolutional Tables Ensemble: classification in microseconds—0
Co-occurrence matrix analysis-based semi-supervised training for object detection—0
Cooking Object's State Identification Without Using Pretrained Model—0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
A Spike Learning System for Event-driven Object Recognition—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
CortexNet: a Generic Network Family for Robust Visual Temporal Representations—0
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation—0
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection—0
Co-training Transformer with Videos and Images Improves Action Recognition—0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
Counting the learnable functions of structured data—0
CPWC: Contextual Point Wise Convolution for Object Recognition—0
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data—0
Application of Faster R-CNN model on Human Running Pattern Recognition—0
Affordance Labeling and Exploration: A Manifold-Based Approach—0
Deep learning based infrared small object segmentation: Challenges and future directions—0
CURL: Co-trained Unsupervised Representation Learning for Image Classification—0
A Study of Image Pre-processing for Faster Object Recognition—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
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection—0
Deep Learning for Material recognition: most recent advances and open challenges—0
DAS: A Deformable Attention to Capture Salient Information in CNNs—0
Deep Learning for the Classification of Lung Nodules—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