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 1401–1450 of 2042 papers

TitleStatusHype
Learning to See by Moving—0
Learning to Segment Moving Objects—0
Learning Transferrable Representations for Unsupervised Domain Adaptation—0
Learning Transformation-Aware Embeddings for Image Forensics—0
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images—0
Learning visual biases from human imagination—0
Learning what and where to attend with humans in the loop—0
Teaching What You Should Teach: A Data-Based Distillation Method—0
Learning with Privileged Information for Multi-Label Classification—0
Learning with Recursive Perceptual Representations—0
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision—0
Leveraging 3D Geometric Priors in 2D Rotation Symmetry Detection—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition—0
Lifting Object Detection Datasets into 3D—0
Lift-the-flap: what, where and when for context reasoning—0
LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial Forgery Detection—0
Light Field Distortion Feature for Transparent Object Recognition—0
Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG—0
Linking Entities Across Images and Text—0
LM-MCVT: A Lightweight Multi-modal Multi-view Convolutional-Vision Transformer Approach for 3D Object Recognition—0
Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation—0
Localized random projections challenge benchmarks for bio-plausible deep learning—0
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition—0
Logical recognition method for solving the problem of identification in the Internet of Things—0
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction—0
Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition—0
Look, Remember and Reason: Grounded reasoning in videos with language models—0
Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method—0
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation—0
LSD-Net: Look, Step and Detect for Joint Navigation and Multi-View Recognition with Deep Reinforcement Learning—0
Machine Learning and Big Scientific Data—0
Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California—0
Machine learning with limited data—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve—0
Matching objects across the textured-smooth continuum—0
Material Classification Using Active Temperature Controllable Robotic Gripper—0
MATT-GS: Masked Attention-based 3DGS for Robot Perception and Object Detection—0
Maximum Likelihood Directed Enumeration Method in Piecewise-Regular Object Recognition—0
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets—0
Measurement Bounds for Sparse Signal Reconstruction with Multiple Side Information—0
Measurement-driven Analysis of an Edge-Assisted Object Recognition System—0
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework—0
Measuring Human Perception to Improve Open Set Recognition—0
Medical Deep Learning -- A systematic Meta-Review—0
Merging SVMs with Linear Discriminant Analysis: A Combined Model—0
Mesh Based Semantic Modelling for Indoor and Outdoor Scenes—0
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition—0
Meta-forests: Domain generalization on random forests with meta-learning—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