FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2772036063> ?p ?o ?g. }

• W2772036063 abstract "We propose a Convolutional Neural Network (CNN)-based model RotationNet, which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves the state-of-the-art performance on an object pose estimation dataset. The code is available on https://github.com/kanezaki/rotationnet" @default.
• W2772036063 created "2017-12-22" @default.
• W2772036063 creator A5033986386 @default.
• W2772036063 creator A5045505869 @default.
• W2772036063 creator A5069496423 @default.
• W2772036063 date "2016-03-20" @default.
• W2772036063 modified "2023-10-16" @default.
• W2772036063 title "RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints" @default.
• W2772036063 cites W1494504319 @default.
• W2772036063 cites W1573897183 @default.
• W2772036063 cites W158943247 @default.
• W2772036063 cites W1591870335 @default.
• W2772036063 cites W1629010235 @default.
• W2772036063 cites W1644641054 @default.
• W2772036063 cites W1713526874 @default.
• W2772036063 cites W1920022804 @default.
• W2772036063 cites W1955462214 @default.
• W2772036063 cites W1994002998 @default.
• W2772036063 cites W2021122545 @default.
• W2772036063 cites W2043948511 @default.
• W2772036063 cites W2051425894 @default.
• W2772036063 cites W2066906022 @default.
• W2772036063 cites W2067783472 @default.
• W2772036063 cites W2093725709 @default.
• W2772036063 cites W2099606917 @default.
• W2772036063 cites W2107464713 @default.
• W2772036063 cites W2112074816 @default.
• W2772036063 cites W2117539524 @default.
• W2772036063 cites W2123456673 @default.
• W2772036063 cites W2131024102 @default.
• W2772036063 cites W2140724064 @default.
• W2772036063 cites W2144691386 @default.
• W2772036063 cites W2147629985 @default.
• W2772036063 cites W2156222070 @default.
• W2772036063 cites W2160643963 @default.
• W2772036063 cites W2160821342 @default.
• W2772036063 cites W2165741220 @default.
• W2772036063 cites W2168356304 @default.
• W2772036063 cites W2190691619 @default.
• W2772036063 cites W2194775991 @default.
• W2772036063 cites W2211722331 @default.
• W2772036063 cites W2213271652 @default.
• W2772036063 cites W2394951287 @default.
• W2772036063 cites W2399850298 @default.
• W2772036063 cites W2400418317 @default.
• W2772036063 cites W2433982775 @default.
• W2772036063 cites W2504204199 @default.
• W2772036063 cites W2511691466 @default.
• W2772036063 cites W2518780089 @default.
• W2772036063 cites W2546066744 @default.
• W2772036063 cites W2555254696 @default.
• W2772036063 cites W2560609797 @default.
• W2772036063 cites W2562354316 @default.
• W2772036063 cites W2582398519 @default.
• W2772036063 cites W2618136105 @default.
• W2772036063 cites W2893477965 @default.
• W2772036063 cites W2951261569 @default.
• W2772036063 cites W2952195138 @default.
• W2772036063 cites W2952689920 @default.
• W2772036063 cites W2953100850 @default.
• W2772036063 cites W2962835968 @default.
• W2772036063 cites W2962928871 @default.
• W2772036063 cites W2963632978 @default.
• W2772036063 cites W2964053173 @default.
• W2772036063 cites W603908379 @default.
• W2772036063 cites W612478963 @default.
• W2772036063 cites W89539373 @default.
• W2772036063 doi "https://doi.org/10.48550/arxiv.1603.06208" @default.
• W2772036063 hasPublicationYear "2016" @default.
• W2772036063 type Work @default.
• W2772036063 sameAs 2772036063 @default.
• W2772036063 citedByCount "12" @default.
• W2772036063 countsByYear W27720360632018 @default.
• W2772036063 countsByYear W27720360632019 @default.
• W2772036063 countsByYear W27720360632020 @default.
• W2772036063 crossrefType "posted-content" @default.
• W2772036063 hasAuthorship W2772036063A5033986386 @default.
• W2772036063 hasAuthorship W2772036063A5045505869 @default.
• W2772036063 hasAuthorship W2772036063A5069496423 @default.
• W2772036063 hasBestOaLocation W27720360631 @default.
• W2772036063 hasConcept C111472728 @default.
• W2772036063 hasConcept C119857082 @default.
• W2772036063 hasConcept C138885662 @default.
• W2772036063 hasConcept C142362112 @default.
• W2772036063 hasConcept C153180895 @default.
• W2772036063 hasConcept C153349607 @default.
• W2772036063 hasConcept C154945302 @default.
• W2772036063 hasConcept C177264268 @default.
• W2772036063 hasConcept C189950617 @default.
• W2772036063 hasConcept C199360897 @default.
• W2772036063 hasConcept C2776035091 @default.
• W2772036063 hasConcept C2776214188 @default.
• W2772036063 hasConcept C2776401178 @default.
• W2772036063 hasConcept C2776760102 @default.
• W2772036063 hasConcept C2777212361 @default.
• W2772036063 hasConcept C2781238097 @default.
• W2772036063 hasConcept C41008148 @default.
• W2772036063 hasConcept C41895202 @default.
• W2772036063 hasConcept C52102323 @default.
• W2772036063 hasConcept C81363708 @default.

• next