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 1226–1250 of 2042 papers

TitleStatusHype
Hierarchical Convolutional Features for Visual Tracking—0
Hierarchical Deep Learning Architecture For 10K Objects Classification—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Hierarchically Compositional Tasks and Deep Convolutional Networks—0
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream—0
Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features—0
Hierarchical Novelty Detection for Visual Object Recognition—0
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition—0
Hierarchical Piecewise-Constant Super-regions—0
Hierarchical Prototype Learning for Zero-Shot Recognition—0
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition—0
Highlight Timestamp Detection Model for Comedy Videos via Multimodal Sentiment Analysis—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces—0
How Can CNNs Use Image Position for Segmentation?—0
How Deep is the Feature Analysis underlying Rapid Visual Categorization?—0
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses—0
How does task structure shape representations in deep neural networks?—0
How good are deep models in understanding the generated images?—0
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time—0
How Small is Big Enough? Open Labeled Datasets and the Development of Deep Learning—0
How to deal with glare for improved perception of Autonomous Vehicles—0
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes—0
How well do deep neural networks trained on object recognition characterize the mouse visual system?—0
HRItk: The Human-Robot Interaction ToolKit Rapid Development of Speech-Centric Interactive Systems in ROS—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