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

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Showing 501–550 of 2042 papers

TitleStatusHype
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Noise-Adaptive Intelligent Programmable Meta-Imager—0
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding—0
Background Invariance Testing According to Semantic Proximity—0
Modeling biological face recognition with deep convolutional neural networks—0
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture—0
A neuromorphic approach to image processing and machine vision—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
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 Normalization—0
Semi-supervised Ranking for Object Image Blur AssessmentCode0
A Survey of Task-Based Machine Learning Content Extraction Services for VIDINT—0
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 network—0
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images—0
Learning Iterative Reasoning through Energy MinimizationCode1
Deep Optical Coding Design in Computational Imaging—0
Complementary datasets to COCO for object detectionCode0
RendNet: Unified 2D/3D Recognizer With Latent Space Rendering—0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements—0
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection—0
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networksCode0
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking ConsistencyCode1
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial NoisesCode0
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models—0
A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth—0
Sparse Mixture-of-Experts are Domain Generalizable LearnersCode1
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information—0
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain AdaptationCode1
Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks—0
OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation—0
Robust Sensible Adversarial Learning of Deep Neural Networks for Image ClassificationCode0
Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition—0
The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networksCode0
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments—0
Efficient Gesture Recognition for the Assistance of Visually Impaired People using Multi-Head Neural Networks—0
Embodied vision for learning object representations—0
A Computational Acquisition Model for Multimodal Word CategorizationCode0
Robustness of Humans and Machines on Object Recognition with Extreme Image Transformations—0
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation—0
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning—0
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition—0
Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring—0
Causal Transportability for Visual RecognitionCode1
Can domain adaptation make object recognition work for everyone?—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