K. Omata, T. Furuya and R. Ohbuchi, "Annotating 3D Models and Their Parts via Deep Feature Embedding,"2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China, 2019, pp. 489-494.
doi: 10.1109/ICMEW.2019.00090

ANNOTATING 3D MODELS AND THEIR PARTS VIA DEEP FEATURE EMBEDDING
Kouki Omata, Takahiko Furuya, Ryutarou Ohbuchi
University of Yamanashi
cannotbe18@gmail.com, takahikof@yamanashi.ac.jp, ohbuchi@yamanashi.ac.jp
ABSTRACT
Need to organize 3D shape data has prompted studies oncomparison and retrieval of 3D shape models. Being able toquery 3D shape models by words, in addition to 3D model
examples and 2D sketches, would be quite beneficial. This
paper proposes a method to associate whole 3D models (e.g.,
automobile) as well as their parts (e.g., tire, body, engine)with word labels so that the 3D model can be queried bywords. The associations between 3D shapes and words arelearned from a dataset of 3D models whose whole model andsegmented parts are labeled with words. Feature vectors ofthese words (distributed representation) and feature vectorsof whole and partial geometries of 3D models are embedded,
by Word Shape embedding Network (WSN) into a common
feature embedding space. As the word feature vectors arelearned by Word2Vec trained on Wikipedia corpus, thecommon embedding space can be queried by a wide variety
of words that are not included in the labeled 3D model
dataset. Experimental evaluation has shown that, with theproposed algorithm, 3D shape can be queried by labels ofeither whole or part shape, or labels that are semanticallyclose but not included in the original 3D model dataset.
Index Terms— 3D model retrieval, 3D shape similarity
comparison, label propagation, deep feature embedding.
1. INTRODUCTION
Prevalent modalities that have been tried for querying 3D
shapes have been 3D shape examples and 2D sketches, bothof which could specify geometric shape in one way or
another. However, these query modalities are not the mostconvenient for human beings to use. A user typically does
not have 3D shape close enough in shape to the desired 3Dmodels. Sketches of 3D models are more accessible, but
sketches, with their abstraction, variation in drawing style,etc. are difficult to compare with 3D shape. It is thus
beneficial if 3D models can be queried by words. To do so,
we must somehow associate 3D shape with words.
Previous work on labeling 3D models known to theauthors associates 3D model with words [1][2] used
geometric similarity of (whole) shape of 3D models to
propagate word annotations from labeled 3D models to
unlabeled 3D models. While these approaches achieved
certain retrieval accuracy when queried using the words

contained in the labeled training 3D model dataset. However,the set of words is very small and limited, and the words areassociated only with the whole 3D shape. Queries based onpart name, or queries based on synonyms or hypernyms can’t
be done.
This paper explores an approach to text-based 3D model
query by associating meaningful textual labels to 3D models(e.g., airplane) and their meaningful parts (e.g., wing, body,tail, engine, …) in labeled 3D model datasets (Figure 1).
Query can be made by words originally contained in thelabeled 3D model dataset the method is trained with, or it canbe queried by words that are close in meaning to those words.
For example, a 3D model of airplane may be queried byoriginal labels of either whole shape (“airplane”) or partial
shapes (“body”, “wing”, etc.). Or, it can be queried by related
words such as “fighter”, “plane”, “airfoil” (synonym of apart), or “vehicle” (hypernym). To this end, the proposed
algorithm embeds feature vectors of both 3D geometry andwords into a common embedding space by the Word Shapeembedding Network (WSN), a triplet of Deep Neural
Networks (DNNs) (See Figure 2). The common feature
space is necessary to compare words and 3D shapes that are
inherent different in their nature. The 3D geometry feature
vectors of both whole and partial shape of 3D models are
extracted by using a DNN. The feature vectors of words, or
distributed representation of words, are computed fromwords associated with labels of 3D model database as well
as a text corpus (e.g., Wikipedia snapshot).
The following of this paper is structured as follows. In
the next section, related work is presented. It is followed byproposed method in Section 3, experimental evaluation in
Section 4, and summary and conclusion follow in Section 5.
Whole shape labels“airplane”“vehicle”“fighter”“body”“engine”“tail”“wing”“airfoil”

Partial shape labels

Fig. 1. Proposed method relates textual labels to both wholeshape (“airplane”) and its part (“body”, “wing”, etc.).

K. Omata, T. Furuya and R. Ohbuchi, "Annotating 3D Models and Their Parts via Deep Feature Embedding,"2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China, 2019, pp. 489-494.
doi: 10.1109/ICMEW.2019.00090
2. RELATED WORK
2.1. Annotating 3D CAD models with words
In their pioneering work, Goldfeder et al. [1] added wordlabels to 3D models without labels (unlabeled 3D models)
via shape similarity comparison of 3D models. Given a setof labeled 3D models, those words are propagated to theirrespective k-nearest neighbor 3D models based on theirsimilarity in their shape feature space. Ohbuchi et al [2]followed up on Goldfeder’s work by using salient feature
space induced by manifold learning of shape feature vectors
for label propagation. Since the training dataset contain bothlabeled and unlabeled dataset, the method of [2] is a form ofsemi-supervised learning. Both these two methods have twolimitations; only whole shape of 3D models are labeled, andthat only those labels that appeared in the training set can beused to label (unlabeled) 3D models.
Using the proposed method, 3D models may be queriedby labels of either their whole shape or partial shape, and thatthe set of words is expanded to include those semanticallyrelated words not found in the training 3D model dataset.
2.2. 3D shape classification and segmentation
Earlier work on 3D shape classification and segmentation
using DNN, such as 3D ShapeNets [11] by Wu and VoxNet[12] by Maturana, employed voxel representation as their
input 3D shape representation. Voxels may not be the best
shape representation, however. Conversion from such 3D
shape representation as polygonal surfaces into voxels
inherently involve approximation and simplification, losing
significant shape details in the process. This is especially true
if voxel resolution is limited to about 323 or so.
Recently, DNNs that could directly accept suchalternative shape representations as point set and triangularmesh have appeared. PointNet [5] and SO-net [13] accepts
3D point set as their input, while FeaStNet [14] accepts
polygonal mesh as its input by using graph convolution.
DLAN [3] and DLSF [10] accepts polygonal mesh as their
input, but internally convert it into 3D oriented point
representation to extract hand-crafted local geometricalfeatures. These features are then processed by DNNs. DLSF
is inherently invariant to rotation and robust against
articulation thanks to its hand-crafted feature.
In this paper, we employ PointNet [5], with amodification, to extract part and whole shape features.
Polygonal mesh 3D models in databases are converted into
point set representation before they are given to PointNet.
PointNet features are unaffected by order of input 3D points
due to maximum pooling in the network. Significantly,
PointNet could also perform segmentation of 3D point set
into meaningful parts if trained by using a training dataset ofsegmented 3D models, such as ShapeNetPart [7]. Weleverage this ability of segmentation for identifying andlabeling parts of 3D models.

2.2. Distributed representation of word
In natural language processing, a distributed representation
is a feature vector of a word in an embedding space of words.
Word2Vec [4], fastText [8], and Doc2Vec [9] are examples
of methods to obtain word feature vector. These methods aretrained on a large text corpus, e.g., a snapshot of Wikipedia.
In the proposed method, we use Word2Vec to produce
word feature vectors to be embedded into a common
embedding space of 3D shape and words.
3. PROPOSED METHOD
3.1. Overall approach
Figure 2 shows the overall processing flow of the proposed
algorithm using WSN. It consists of the following three parts,
feature extraction, feature embedding, and labeling.
(1) Feature extraction: Word feature vectors are
extracted from words associated with 3D models in the
training set using Word2Vec [4]. To train WSN, word
features of the labeled 3D models and their parts in the
training dataset are extracted. In the retrieval phase, word
feature of the query word is extracted.
Also, for each 3D model in the dataset, a shape featureof an entire 3D model (“whole shape feature”) and shapefeatures of the model’s meaningful parts (“partial shapefeature”) are extracted using modified PointNet.
(2) Feature embedding: Word as well as shape features
are embedded into a common embedding feature space by
the Word Shape embedding Network (WSN) so they canbe compared. The WSN consists of three neural nets, oneeach for word, whole shape, and partial shape. The WSN
is trained by using contrastive loss [16] so that the wordand shape having the same training label are close, while
those having different labels are distant.
(3) Retrieval / Labeling: After training the WSN,
features of both 3D shape models and words areembedded in the common embedding feature space, readyfor comparison. For word query based retrieval, given aword as the query, its feature in the embedding space iscomputed. Then, shape features, either of whole or partial,
closest to the word feature in the embedding space by L2norm are retrieved.
To label an unlabeled 3D shape, choose its shapefeature in the embedding space, and find those words thatare closest to the shape feature as the shape’s label.
This approach allows any word in a rich text corpus used
to train Word2Vec to be used as query. A 3D model could ofcourse be queried by using words contained as labels ofwhole or partial shapes of the 3D model. In addition, a rich
set of words, including synonyms, hypernyms, or hyponyms
of words used as labels, may also be used to query the 3Dmodel. Note that this is not the case for the earlier algorithm

K. Omata, T. Furuya and R. Ohbuchi, "Annotating 3D Models and Their Parts via Deep Feature Embedding,"2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China, 2019, pp. 489-494.
doi: 10.1109/ICMEW.2019.00090
Feature extraction
Extract
word feature“engine”
Partial shapelabel“airplane”
Whole shapelabel

Word2Vec Word feature(200dim)
Extract
word feature
Word feature(200dim)Word2Vec
Extract wholeshape feature(whole shape)
3D point set

EmbeddingWord-shape distancecomparison
vehicleShared parameters

W-block

WSN

Whole shapemodified
PointNet feature (256dim)
Extract partial
shape feature

S-block (W)Partial shapefeature (128dim)

S-block (P)modified
PointNet

Labelingbody
Common feature embedding
space (1,000dim)

engineairplane

wing

tail

Labeled 3D model

Embedding DNN trained using contrastive loss
Negative pairs further Positive pairs closer“guitar”“airplane”

Fig. 2. Feature vectors of labels of whole shape (“airplane”) and partial shapes (“body”, “wing”, etc.) are transformed by Wblock to vectors in the common feature space. 3D shape features of the whole shape and partial shapes are transformed by Sbock (W) and S-block (P), respectively, to vectors in the common feature space.
by Goldfeder [1] or by Ohbuchi [2]. In these algorithms, only
small set of words used as labels of 3D models in a databasecan be used for queries.
3.2. Shape and word feature embedding network WSN
WSN consists of three subparts; (1) W-block, or Word
embedding block, for word embedding, (2) S-block(P), or
shape embedding block for partial shape, and (3) SBlock(W), or shape embedding block for whole shape. Wblock accepts a word feature vector and transforms it to afeature vector in the common feature embedding space via 4layer fully-connected neural network. S-block(W) and Sblock(P), both of which are also 4-layer fully connectedneural networks, accept whole and partial shape feature
vectors, respectively, and transform them to vectors in thecommon embedding feature space. No parameter sharing isdone among W-block, S-block(P) and S-block(W).
We set the dimensionality of word feature of Word2Vec
at 200. Dimensionality of whole shape feature and partial
shape feature are set at 256 and 128, respectively. The
dimensionality of common embedding space is chosen byexperiments as 1,000 from a few alternatives.
(Comprehensive search for optimal values of all the hyper
parameters of WSN is too expensive for us to conduct.)
The number of neurons are, for W-block 200-1,0004,000-1,000), for S-block(W) 256-2,000-8,000-1,000, andfor S-block(P) 128-2,000-8,000-1,000. We used Rectified
Linear Unit (ReLU) activation function [15] for all theneurons. Activation of the last layer’s 1,000 neurons are L2normalized to produce 1,000 dimensional vector for theembedding feature space. We note that, while far fromcomprehensive, we experimented with several different
network architectures for the WSN to find the best
performing one.
3.3. 3D shape feature extraction
Whole and partial 3D shape features are extracted by using a
network based on PointNet [6]. PointNet accepts a set of 3D
points, and recognizes its whole shape. Whole shape feature
is simply activation of the last fully connected layer of the(standard) PointNet. We train the PointNet for (whole) shaperecognition, and use the activation of 256 neurons of the last
fully connected layer of the PointNet as 256 dimensionalfeature vector.
PointNet can segments a point set into meaningful partsby adding labels of the parts to each 3D point. This of course
requires supervised training using segmented dataset such as
ShapeNetPart [7]. Our algorithm regards such a segment
generated by PointNet as a partial shape. If a whole 3D
model is segmented into n parts, n partial shape features will
be generated for the model. To obtain a partial shape featureof a segmented part, we aggregate all the per-point shapefeature of points contained in the segment by using maxpooling (See Figure 3.) Each per-point shape feature vector
is a set of activation of the last layer of per-point
segmentation network of the PointNet. We aggregate all the
per-point shape features of points having the same
segmentation label by using the max-pooling layer. As perpoint feature has dimensionality of 128, an aggregated persegment feature vector also has dimensionality of 128.

K. Omata, T. Furuya and R. Ohbuchi, "Annotating 3D Models and Their Parts via Deep Feature Embedding,"2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China, 2019, pp. 489-494.
doi: 10.1109/ICMEW.2019.00090
PointNet

a whole shape feature (256dim)

whole shaperecognition net

per-segment (part)max-poolingpoint-wisesegmentation net…
P
per-point features(128dim)segment label per pointpart 1 part 2 … part npart 1
part 2part 4part 4#ofsegmented
parts n=4

n partial shapefeatures (n=4)(128dim)Fig. 3. A partial shape feature is an aggregation of per-point
shape features contained in a segmented part (e.g., wing).

pairs improve labeling accuracy quite significantly (in fmeasure, from 54.3% to 63.1%).
The number of 3D models in ShapeNetPart, is not large
enough for training DNNs. We thus perform data
augmentation of 3D models via three methods below.
(1) Scale: Scale the 3D model along one of 6 axes, thatare, x, y, and z coordinate axes and three principal axes
computed via principal component analysis (PCA) of
point distribution of each 3D point set model.
(2) Bending: Bend the 3D model off from a plane
determined by the principal axes. Control points for thebending, and the direction and amount of displacement
are determined randomly. The following pictures show
original (left) and exaggerated examples of bending.

3.4 Word feature extraction
Word feature, or distributed representation of word, having
dimensionality 200 is computed by using Word2Vec [4]. We
chose Word2Vec over fastText, for the former showed better
accuracy in our preliminary experiment (63.1% over 60.4%
in f-measure for whole shape labeling accuracy).
Some of the words that appeared in the labels of 3Dmodels are not found in the training set of the pre-trained
Word2Vec model. We located sentences in the Word2Vec
training set that contain synonyms of those unfound words,
and replaced these synonyms with the unfound words of the
3D model labels. We then retrained the Word2Vec model.
For the training, we used CBOW having window size 4.
3.5. Training WSN
WSN is trained to embed heterogeneous features of shape
and word by using contrastive loss [16]. Training data forcontrastive loss are pairs of data, that are either positive or
negative. A positive pair consists of a shape and its correctlabel, e.g., a 3D model of human and the word label “human”.
A negative pair consists of a shape and an unrelated word,e.g., a human model paired with the word “automobile”.
An effective training requires (1) equal number of pairs
across classes, and (2) equal number of negative and positive
pairs. ShapeNetPart [7], with its segmentation and labeling,has significant variation in sizes of classes. Furthermore,using a straightforward pairing strategy, about 200k positive
pairs and 11,000k negative pairs are generated.
To smooth out class imbalance, we try to match sizes ofclasses to that of the class having the largest number of
positive pairs. To do so, we increase number of positive pairs
in classes having smaller number of positive pairs by
duplication. After the number of positive pairs are evened out,
negative pairs for the class are generated to match thenumber (after inflation) of the positive pairs. In the end, we
generated about 2 million each of positive as well as negative
pairs for training. These balancing of the number of training(3) Region growing: Segmentation produced by
PointNet is somewhat arbitrary. In an attempt torandomize the segmentation, we grow the size of eachsegmented region randomly using one of several
predetermined methods.
We implemented the WSN using TensorFlow. A hyper
parameter of the contrastive loss, the margin size m, is set at0.07 via preliminary experiments. Optimization of the neuralnetwork loss function is done by using Adam [17] with initial
learning coefficient of 0.0003. All the parameters of theneural network are randomly initialized. The training is done
until decrease in contrastive loss converges. We iterated until
about 200 epochs, which took about 3 days using Intel Core
i7-6700 paired with NVIDIA GeoForce GTX Titan.
4. EXPERIMENTS AND RESULTS
We perform quantitative evaluation of (1) label accuracies ofwhole shape labeling and partial shape labeling, and(2) impact data augmentation has on labeling accuracy.
We also wish to do quantitative analysis of retrieval
accuracy using words semantically related to but different
from word labels attached to the training 3D models.
However, we know of no dataset of 3D models with groundtruth of related semantically words. Instead, we show
examples of query by words including some semanticallyrelated words.
4.1. 3D shape model dataset
For quantitative evaluation of accuracy of whole shapelabeling, we used ShapeNetCore55 [6] dataset. To
qualitatively evaluate whole and partial shape labeling, weused ShapeNetPart [7] dataset (Figure 4 for examples).

K. Omata, T. Furuya and R. Ohbuchi, "Annotating 3D Models and Their Parts via Deep Feature Embedding,"2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China, 2019, pp. 489-494.
doi: 10.1109/ICMEW.2019.00090
The ShapeNetPart is a dataset in which all the 3D models
are segmented into parts, and all the parts of all the models
are semantically labeled. It contains about 15,000 3D models
divided into 16 classes. Polygon mesh models in the databaseare converted into 3D point set representation having 2,048
points by randomly sampling the mesh surfaces to be sent toPointNet. Every point of the converted models are labeled byrespective part label (e.g., “wing”).
ShapeNetCore55 is one of the largest 3D model datasetlabeled by whole shape. Models in the dataset are classified
into 55 classes (“airplane”, “sofa”, “flowerpot”, etc.). The
dataset contains 35,764 model training set and 10,265 model
test set. We use the training set to train WSN, and test set forevaluation. Models in this dataset are also converted to 3D
point sets for processing.
Labelsairplane
wing
body
tail
enginewheel
deckskateboard
skateboard
other

Segmentation

Data augmentation impacted labeling accuracy significantly,
as the Table 3 shows for whole shape label. This experimentuses ShapeNetCore55 dataset and evaluated using retrieval
accuracy queried by whole shape labels. Of the threeaugmentation methods, bending appears the most effective.
Interestingly, when the three augmentation methods are
combined, accuracy improved significantly by nearly 9%.
Table 2. 3D shape data augmentation and accuracy ofwhole shape retrieval.
3D shape augmentation methods
None(1) Scaling(2) Bending(3) Region expansion(1)+(2)+(3)f-measure (%)
68.6
64.4
75.0
69.4
77.4

4.4. Examples of partial shape labeling and word-based
retrieval(axsles are labeled “other”)

Fig. 4. Examples of segmented 3D point set model data
generated from ShapeNetPart [7].
4.2. Whole shape label accuracy
We computed accuracy indices, in F-measure, for when
whole shape label and partial shape labels are inferenced
together given a whole shape 3D point set model.
ShapeNetCore55 dataset is used for this experiment.
Proposed method is compared against Goldfeder-PointNet,
which is the method of Goldfeder [1], except that its shapefeature is “modernized” to the whole shape feature obtained
from the same PointNet as used in the proposed method.
Whole shape label accuracy among the two methods arenearly identical, as depicted in Table 2. This isunderstandable as the k-nearest neighbor is known toperform quite well. The difference is that, while theGoldfeder-PointNet can’t handle partial shape label, ourproposed method can, with the accuracy of 77.4%. We
suspect that the accuracy of 77%, when queried by using
partial shape label, is useful as is for some of the real world
applications.
Table 2. Accuracy of whole shape labeling and partial
shape labeling (f-measure [%]).
Whole shape label

Partial shape label

Goldfeder-PointNet

95.8

Not possible

Proposed

96.0

77.4

Methods

4.3. Impact of data augmentation

Figure 5 shows example of success as well as failure cases
for labeling unlabeled 3D shapes. Upper two rows are
success cases, while lower two rows are failure cases. It
appears that most failures are due to mis-segmentation by
PointNet. Some other failures are due to “incorrect groundtruth” in [8] that are semi-automatically generated.
Table 3 shows examples of retrieval, in which query word
airplane (whole shape label), vehicle (hypernym of wholeshape label), wing (partial shape label), and airfoil (synonymof partial shape label) are used.
Ground truth Gournd truth Segmentation
label
segmentation

Addedlabels

car
roof
hood
wheel

car
roof
hood
wheel
other

guitar
head
body
nec

guitar
head
body
nec

chair
back
arm
seat

chair
back
leg
arm
seat

earphoneearphoneother
headbandearphonemug
peak

Fig. 5. Examples of partial shape labeling. Success case(upper two), and failure case (lower two).

K. Omata, T. Furuya and R. Ohbuchi, "Annotating 3D Models and Their Parts via Deep Feature Embedding,"2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China, 2019, pp. 489-494.
doi: 10.1109/ICMEW.2019.00090[5] C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet:
Deep learning on point sets for 3d classification andsegmentation,” Proc. CVPR 2017, pp. 77-85, 2017.
[6] A. X. Chang, et al. “ShapeNet: An Information-Rich 3D
Model Repository”, arXiv:1512.03012, 2015.
[7] L. Yi, et al. “A scalable active framework for region
annotation in 3D shape collections.” SIGGRAPH Asia,
2016, 2016.
[8] Piotr Bojanowski, et al. “Enriching Word Vectors with
Subword Information.” arXiv:1607.04606v1. 2016.
[9] Q. V. Le and T. Mikolov. “Distributed Representationsof Sentences and Documents”, 1st Workshop on
Representation Learning for NLP. 2015.
[10] E. Rodolà, et al., “Deformable Shape Retrieval with
Missing Parts”, Proc. EG 3DOR 2017. 2017.
[11] Z. Wu, et al. 3D ShapeNets: A deep representation forvolumetric shape modeling. Proc. CVPR 2015, 2015.
[12] D. Maturana and S. Scherer. VoxNet: A 3D
Convolutional Neural Network for Real-Time Object
Recognition. Proc. IROS 2015, pp.922–928, 2015.
[13] J. Li, B. M. Chen, G. H. Lee. “SO-Net: Self-Organizing
Network for Point Cloud Analysis” Proc. CVPR 2018.
[14] N. Verma et al. “FeaStNet: Feature-Steered Graph
Convolutions for 3D Shape Analysis” Proc. CVPR 2018.
[15] V. Nair and E. G. Hinton. “Rectified linear units
improve restricted Boltzmann machines”, Proc ICML
2010, 2010.
[16] R. Hadsell, S. Chopra, and Y. LeCun. “Dimensionality
Reduction by Learning an Invariant Mapping”, Proc.
CVPR 2006, 2006.
[17] P. D. Kingma and B. J. Lei. “Adam: A Method for
Stochastic Optimization”, Proc. ICLR 2015, 2015.

5. CONCLUSION AND FUTURE WORKIn this paper, we proposed a method to associate whole 3D
models (e.g., automobile) as well as their parts (e.g., tire,body, engine) with word labels so that the 3D model can bequeried by words. The words and shape are related in acommon embedding space induced by both Word2Vec word
features trained on Wikipedia corpus and whole as well aspart shape features of segmented 3D model corpus.
Experimental evaluation showed that the proposed algorithm
achieved query by word retrieval accuracy, in f-measure, of
96% for whole shape and 77% for partial shape.
We would also like to quantitatively evaluate theaccuracy of query by using semantically related words, e.g.,synonyms or hypernyms, that are not used in the labels of
training 3D models. This is currently difficult for there is nodatabase of 3D models having ground truth labels of thesesemantically related words. We also would like to use large
collection of 3D models for both training and testing.
REFERENCES[1] C. Goldfeder and P. Allen. “Autotagging to Improve
Text Search for 3D Models”, JCDL 2008, 2008.
[2] R. Ohbuchi, and S. Kawamura. “Shape-Based Autotagging og 3D Models for Retrieval” Proc. SAMT 2009.
[3] T. Furuya, and R. Ohbuchi. “Deep Aggregation of Local
3D Geometric Features for 3D Model Retrival.” Proc.
BMVC 2016, 2016.
[4] T. Mikolov, et al. “Distributed Representations ofWords and Phrases and their Compositionality”, Proc.
NIPS 2013, 2013.

Table 3. Examples of query by whole shape label, synonym of whole shape label, partial shape label, and synonym of partial shape label.
Query/rank
airplane(whole shapelabel)
vehicle(synonym ofwhole shapelabel)
wing(partial shapelabel)
airfoil(synonymof partial
shape label)

1

2

3

4

5

6











airplane

airplane

airplane

airplane

airplane
airplane
motorbike
airplanepistol
airplane
airplane
airplane
airplane
airplane
airplane

airplane

airplane

airplane

airplanerocketairplanecar
airplane

airplane?
rocket?

?
rocket8
airplane

airplane

airplane

airplane







7

airplane
airplane