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 501525 of 2042 papers

TitleStatusHype
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding0
Noise-Adaptive Intelligent Programmable Meta-Imager0
Background Invariance Testing According to Semantic Proximity0
Modeling biological face recognition with deep convolutional neural networks0
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture0
A neuromorphic approach to image processing and machine vision0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks0
Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point CloudCode2
Omni3D: A Large Benchmark and Model for 3D Object Detection in the WildCode2
Learning Counterfactually Invariant PredictorsCode1
Contributions of Shape, Texture, and Color in Visual RecognitionCode1
Easy Batch Normalization0
Semi-supervised Ranking for Object Image Blur AssessmentCode0
A Survey of Task-Based Machine Learning Content Extraction Services for VIDINT0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Multi-area Target Individual Detection with Free Drawing on VideoCode0
SESS: Saliency Enhancing with Scaling and SlidingCode0
Simulating reaction time for Eureka effect in visual object recognition using artificial neural network0
Learning Iterative Reasoning through Energy MinimizationCode1
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images0
Deep Optical Coding Design in Computational Imaging0
Complementary datasets to COCO for object detectionCode0
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements0
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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