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 326350 of 2042 papers

TitleStatusHype
FPNN: Field Probing Neural Networks for 3D DataCode0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
Foveated Instance SegmentationCode0
Generalisation in humans and deep neural networksCode0
Fit to Measure: Reasoning about Sizes for Robust Object RecognitionCode0
An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation DatasetCode0
Food Image Recognition by Using Convolutional Neural Networks (CNNs)Code0
Feature Pyramid GridsCode0
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural networkCode0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Feature Learning by Multidimensional Scaling and its Applications in Object RecognitionCode0
Fast Feature Fool: A data independent approach to universal adversarial perturbationsCode0
Feature Learning for Accelerometer based Gait RecognitionCode0
Finding Tiny FacesCode0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural NetworksCode0
Exploring Novel Object Recognition and Spontaneous Location Recognition Machine Learning Analysis Techniques in Alzheimer's MiceCode0
Facial Expression Recognition Research Based on Deep LearningCode0
Foveation in the Era of Deep LearningCode0
Chessboard and chess piece recognition with the support of neural networksCode0
THOR2: Topological Analysis for 3D Shape and Color-Based Human-Inspired Object Recognition in Unseen EnvironmentsCode0
Characterizing and evaluating adversarial examples for Offline Handwritten Signature VerificationCode0
Experiments with mmWave Automotive Radar Test-bedCode0
Generalizing to unseen domains via distribution matchingCode0
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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