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 1351–1400 of 2042 papers

TitleStatusHype
Latent Bi-constraint SVM for Video-based Object Recognition—0
Latent Cognizance: What Machine Really Learns—0
Latent Constrained Correlation Filter—0
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning—0
Layerwise complexity-matched learning yields an improved model of cortical area V2—0
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning—0
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks—0
Learning about Canonical Views from Internet Image Collections—0
Learning a discriminative hidden part model for human action recognition—0
Learning and Calibrating Per-Location Classifiers for Visual Place Recognition—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—0
Learning Attributes Equals Multi-Source Domain Generalization—0
Learning Beyond Human Expertise with Generative Models for Dental Restorations—0
Learning by Asking Questions for Knowledge-based Novel Object Recognition—0
Learning Canonical 3D Object Representation for Fine-Grained Recognition—0
Learning Collections of Part Models for Object Recognition—0
Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks—0
Learning data association without data association: An EM approach to neural assignment prediction—0
Learning Deep Features for Scene Recognition using Places Database—0
Learning Descriptors for Object Recognition and 3D Pose Estimation—0
Learning Detailed Face Reconstruction from a Single Image—0
Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels—0
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization—0
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision—0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Learning Good Representation via Continuous Attention—0
Learning Hard Alignments with Variational Inference—0
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction—0
Learning Illuminant Estimation from Object Recognition—0
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics—0
Learning in Markov Random Fields using Tempered Transitions—0
Learning invariant representations and applications to face verification—0
Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes—0
Learning Multi-Scale Representations for Material Classification—0
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction—0
Learning Receptive Fields for Pooling from Tensors of Feature Response—0
Learning Relationships for Multi-View 3D Object Recognition—0
Learning Representations of Graph Data -- A Survey—0
Learning Robust Object Recognition Using Composed Scenes from Generative Models—0
Learning Scalable Discriminative Dictionary with Sample Relatedness—0
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition—0
Learning Semantic Similarities for Prototypical Classifiers—0
Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition—0
Learning to Combine Mid-Level Cues for Object Proposal Generation—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
Learning to Learn with Compound HD Models—0
Learning to Localize and Align Fine-Grained Actions to Sparse Instructions—0
Learning to Recognize Objects by Retaining other Factors of Variation—0
Learning to Represent Image and Text with Denotation Graph—0
Learning to see across Domains and Modalities—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