Learning Compact Geometric Features Supplementary Material
نویسندگان
چکیده
Recall that our proposed input parameterization is R = 17 subdivisions in the radial direction, E = 11 in the elevation direction, A = 12 in the azimuth direction, and a search radius of 17% of the diameter of the model. The resultant dimensionality of the input histogram is N = 2,244. In each experiment we select one of these parameters and consider the parameterization resulting from changing this value by ±2.
منابع مشابه
Supplementary Material for On Compact Codes for Spatially Pooled Features
001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 1...
متن کاملCombining Geometric, Textual and Visual Features for Predicting Prepositions in Image Descriptions: Supplementary Material
Prepositions in Image Descriptions: Supplementary Material Arnau Ramisa* Josiah Wang* Ying Lu Emmanuel Dellandrea Francesc Moreno-Noguer Robert Gaizauskas 1 Institut de Robòtica i Informàtica Industrial (UPC-CSIC), Barcelona, Spain 2 Department of Computer Science, University of Sheffield, UK 3 LIRIS, École Centrale de Lyon, France {aramisa, fmoreno}@iri.upc.edu {j.k.wang, r.gaizauskas}@sheffie...
متن کاملSupplementary Material: Unsupervised learning of object landmarks by factorized spatial embeddings
In this supplementary material we elaborate on several details regarding the experimental setup, provide an additional comparison with training a supervised network on small numbers of images and present numerous images giving a qualitative look at the performance of our method. It is organized as follows: Sec. 2 gives the additional details and hyperparameters, Sec. 3 compares quantitatively w...
متن کاملIntroduction to Information Visualization supplementary material
History of effort to store and visualize some information begin deep in the times when Babylon has been considered as the middle of known world. Since then many documents preserve, helping to create compact picture of the Information visualization (hereafter InfoVis) evolution. With the steep growth of data size in the second half of 20th century one question began to be more and more important...
متن کاملOnline Streaming Feature Selection Using Geometric Series of the Adjacency Matrix of Features
Feature Selection (FS) is an important pre-processing step in machine learning and data mining. All the traditional feature selection methods assume that the entire feature space is available from the beginning. However, online streaming features (OSF) are an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with t...
متن کامل