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 776–800 of 2042 papers

TitleStatusHype
Comparative evaluation of CNN architectures for Image Caption GenerationCode0
Approximation of dilation-based spatial relations to add structural constraints in neural networks—0
The Effects of Image Distribution and Task on Adversarial Robustness—0
Efficient Online ML API Selection for Multi-Label Classification Tasks—0
Learning Visual Models using a Knowledge Graph as a Trainer—0
Grid Cell Path Integration For Movement-Based Visual Object RecognitionCode0
Comparison of semi-supervised deep learning algorithms for audio classificationCode1
An ecologically motivated image dataset for deep learning yields better models of human vision—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
A Too-Good-to-be-True Prior to Reduce Shortcut Reliance—0
Audiovisual Highlight Detection in Videos—0
What does LIME really see in images?Code0
Enhancing efficiency of object recognition in different categorization levels by reinforcement learning in modular spiking neural networks—0
Content Placement in Networks of Similarity Caches—0
Large Scale Long-tailed Product Recognition System at Alibaba—0
Overhead MNIST: A Benchmark Satellite Dataset—0
Sill-Net: Feature Augmentation with Separated Illumination RepresentationCode1
Zero-shot Learning with Deep Neural Networks for Object Recognition—0
The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs—0
ISP Distillation—0
A Spike Learning System for Event-driven Object Recognition—0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels MethodsCode0
A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application—0
Machine learning with limited data—0
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape BiasCode1
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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