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 676–700 of 2042 papers

TitleStatusHype
A large comparison of feature-based approaches for buried target classification in forward-looking ground-penetrating radar—0
CoTDet: Affordance Knowledge Prompting for Task Driven Object Detection—0
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation—0
Assessing The Importance Of Colours For CNNs In Object Recognition—0
CortexNet: a Generic Network Family for Robust Visual Temporal Representations—0
Correlated and Individual Multi-Modal Deep Learning for RGB-D Object Recognition—0
ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels—0
A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition—0
A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS—0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
A Spike Learning System for Event-driven Object Recognition—0
Cooking Object's State Identification Without Using Pretrained Model—0
Co-occurrence matrix analysis-based semi-supervised training for object detection—0
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation—0
Airplane Type Identification Based on Mask RCNN; An Approach to Reduce Airport Traffic Congestion—0
Convolutional Tables Ensemble: classification in microseconds—0
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks—0
Convolutional Prototype Learning for Zero-Shot Recognition—0
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling—0
A Simulation Method for MMW Radar Sensing in Traffic Intersection Based on BART Algorithm—0
AIris: An AI-powered Wearable Assistive Device for the Visually Impaired—0
Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects—0
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future—0
Convolutional Networks with Dense Connectivity—0
A ``Shape Aware'' Model for semi-supervised Learning of Objects and its Context—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