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

Showing 176200 of 2042 papers

TitleStatusHype
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic SurveyCode1
Deep Predictive Coding Networks for Video Prediction and Unsupervised LearningCode1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny ObjectsCode1
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group SoftmaxCode1
Learning what and where to attendCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
From Chaos Comes Order: Ordering Event Representations for Object Recognition and DetectionCode1
Rehearsal-Free Continual Learning over Small Non-I.I.D. BatchesCode1
PartImageNet++ Dataset: Scaling up Part-based Models for Robust RecognitionCode1
Describing Textures in the WildCode1
Are Convolutional Neural Networks or Transformers more like human vision?Code1
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object DetectionCode1
Causal Transportability for Visual RecognitionCode1
Paxion: Patching Action Knowledge in Video-Language Foundation ModelsCode1
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object RecognitionCode1
Recognize Any RegionsCode1
Divergences in Color Perception between Deep Neural Networks and HumansCode1
Distributed Deep Neural Networks over the Cloud, the Edge and End DevicesCode1
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance SegmentationCode1
DOCTOR: A Simple Method for Detecting Misclassification ErrorsCode1
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Robust and efficient post-processing for video object detectionCode1
Going Deeper with ConvolutionsCode1
MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified