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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–725 of 2042 papers

TitleStatusHype
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition—0
CNN-based search model underestimates attention guidance by simple visual features—0
A neuromorphic approach to image processing and machine vision—0
Advancing Egocentric Video Question Answering with Multimodal Large Language Models—0
Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities—0
Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching—0
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users—0
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data—0
Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning—0
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition—0
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN—0
EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections—0
EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects—0
Distributed Coding of Multiview Sparse Sources with Joint Recovery—0
A Neuro-AI Interface: Learning DNNs from the Human Brain—0
A Voxel Graph CNN for Object Classification with Event Cameras—0
Exact neural mass model for synaptic-based working memory—0
Combinatorial clustering and the beta negative binomial process—0
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment—0
A Dual-hierarchy Semantic Graph for Robust Object Recognition—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