# Attribute-Guided Multi-Level Attention Network
for Fine-Grained Fashion Retrieval

Ling Xiao and Toshihiko Yamasaki
Department of Information and Communication Engineering,
Graduate School of Information Science and Technology,
The University of Tokyo, Tokyo, Japan

###### Abstract

This paper proposes an attribute-guided multi-level attention network (AG-
MLAN) to learn fine-grained fashion similarity. AG-MLAN is able to make a more
accurate attribute positioning and capture more discriminative features under
the guidance of the specified attribute. Specifically, the AG-MLAN contains
two branches, branch 1 aims to force the model to recognize different
attributes, while branch 2 aims to learn multiple attribute-specific embedding
spaces for measuring the fine-grained similarity. We first improve the
Convolutional Neural Network (CNN) backbone to extract hierarchical feature
representations, then the extracted feature representations are passed into
branch 1 for attribute classification and branch 2 for multi-level feature
extraction. In branch 2, we first propose a multi-level attention module to
extract a more discriminative representation under the guidance of a specific
attribute. Then, we adopt a masked embedding module to learn attribute-aware
embedding. Finally, the AG-MLAN is trained with a weighted loss of the
classification loss in branch 1 and the triplet loss of the masked embedding
features in branch 2 to further improve the accuracy in attribute location.
Extensive experiments on the DeepFashion, FashionAI, and Zappos50k datasets
show the effectiveness of AG-MLAN for fine-grained fashion similarity learning
and its potential for attribute-guided retrieval tasks. The proposed AG-MLAN
outperforms the state-of-the-art methods in the fine-grained fashion
similarity retrieval task.

##  1 Introduction

Similarity retrieval is an important research topic in the field of fashion
[19, 6, 16, 5, 8, 35], especially for in-shop clothes retrieval [19, 2] and
cross-domain fashion retrieval [13, 15, 22]. Currently, the mainstream
approaches in fine-grained fashion retrieval aim to learn a general embedding
space to compute the similarity among fashion items, and the triplet loss is
always adopted to train the model [38, 10]. However, the majority of methods
are proposed to learn the overall similarity. In this paper, we aim for fine-
grained fashion similarity learning, such as neckline design, pant length,
lapel design, and so on. Fine-grained fashion retrieval pays attention to the
specific regions and features in the image under specific attribute guidance
[30, 20, 31, 36]. Specifically, the fine-grained similarity is also important
in fashion copyright protection scenario [21], which can find items with
plagiarized designs. Hence, learning fine-grained similarity is necessary.

Veit et al. [30] first learned an overall embedding space, and then employed a
fixed mask to select relevant embedding dimensions w.r.t. the specified
attribute. The fine-grained similarity is measured in terms of the masked
embedding feature. Ma et al. [20] went further in this direction, they
proposed to learn multiple attribute-specific embedding spaces and measure the
fine-grained similarity in the corresponding space. Wan et al. [31] proposed
an attention-based attribute-guided fashion similarity learning network for
fine-grained fashion retrieval as well. Yan et al. [36] employed an iterative
learning manner to make up for the shortcomings of the traditional attention
mechanism, resulting in a more accurate attribute positioning. However, there
is still much space to improve the accuracy of fine-grained fashion similarity
learning.

In this work, we propose an attribute-guided multi-level attention network
(AG-MLAN) to learn fine-grained fashion similarity. Specifically, we propose
two branches. Branch 1 forces the model to recognize different attributes with
the extracted hierarchical feature representations. While branch 2 is a multi-
level attention network that aims to learn multiple attribute-specific
embedding spaces for measuring fine-grained similarity. In branch 2, we first
introduce a multi-level attention module to extract attribute-specific
representations. Then, a fixed mask is applied to each attribute-specific
embedding space to further improve the positing ability of the feature. The
AG-MLAN learns multiple attribute-specific embeddings in an end-to-end manner
and is trained with a weighted loss of the classification loss and the triplet
loss of the masked embedding features.

In summary, this paper presents the following contributions:

1\. We improve the CNN backbone to extract hierarchical feature
representations and propose a classification branch to force the model to
recognize different attributes.

2\. A multi-level attention module is introduced to extract attribute-specific
embedding that can better locate the related regions with a given attribute.

3\. We propose to add a masked embedding module to further improve the
positioning accuracy of the extracted attribute-specific embedding.

4\. We propose an AG-MLAN model for fine-grained fashion similarity learning.
AG-MLAN outperforms the state-of-the-art methods in fine-grained fashion
retrieval. It can make a more accurate attribute positioning and capture more
discriminative features under the guidance of the specified attribute.

##  2 Related work

###  2.1 Fashion retrieval

Many fashion retrieval methods that focus on the overall similarity of fashion
images have been proposed in recent years [6, 38, 10, 15, 14, 25]. For
example, in the cross-domain retrieval task, Ji et al. [15] utilized the rich
label information on the e-commerce website to assist the attention mechanism
to locate the clothing in the complex scene and then complete the in-shop
retrieval according to the extracted clothing features. In the fashion
compatibility task, given the fashion items in an outfit, Han et al. [10]
trained a bi-directional long short-term memory (Bi-LSTM) model to
sequentially predict the next item conditioned on previous ones to learn their
compatibility relationships. The above-mentioned work mainly focuses on the
similarity of the whole image. Later, the emergence of the attention mechanism
improves the positioning ability of the model and is widely used in computer
vision [34, 24, 3, 28] and recommendation system [40, 27, 18]. Wang et al.
[33] employed a self-learning visual attention model to extract clothing
features without external landmark labeling. However, this attention mechanism
only focuses on the overall similarity of clothing and can not meet the needs
of fine-grained retrieval.

###  2.2 Attention mechanism

Recently attention mechanism has become a popular technique and showed
superior effectiveness in various research areas, such as computer vision [34,
32, 39, 23, 17, 12] and natural language processing [29, 7, 26]. To some
extent, attention can be regarded as a tool to bias the allocation of the
input information. As fashion images always present with complex backgrounds,
pose variations, etc., using a fixed region for a specific attribute for all
images is not optimal, thus attention mechanism is also common in the fashion
domain [15, 33, 9, 1, 2, 20]. For instance, Ak et al. [2] used the prior
knowledge of clothes structure to locate the specific parts of clothes.
However, their approach can only be used for upper-body clothes thus limiting
its generalization. Wang et al. [33] proposed to learn channel attention
implemented by a fully convolutional network. Ji et al. [15] utilized
attributes to facilitate attention modeling, but they use all attributes of
fashion items. In Ma et al. [20], they proposed two attribute-aware attention
modules, which utilize a specific attribute as the extra input in addition to
a given image. The proposed attention modules capture the attribute-related
patterns under the guidance of the specified attribute. Wan et al. [31]
proposed two attribute-aware attention modules as well. Yan et al. [36]
employed an iterative learning manner to the channel attention module for a
more accurate attribute positioning. Different from the existing methods, we
introduce a multi-level attention module and build coarse to fine attention
for a more accurate attribute positioning and more discriminative embedding
space under the guidance of the specified attribute.

##  3 Proposed method

###  3.1 Overview

Given an image $I$ and a specific attribute $a$, we propose to learn an
attribute-specific feature vector $f(I,a)\in\mathbb{R}^{c}$ which reflects the
characteristics of the corresponding attribute in the image. $c$ is the number
of channels. Therefore, for two fashion images $I$ and $I^{{}^{\prime}}$, the
cosine similarity between $f(I,a)$ and $f(I^{{}^{\prime}},a)$ are used to
represent the fine-grained similarity. Moreover, the fine-grained similarity
for multiple attributes can be computed by summing up the similarity scores on
the individual attributes. Note that the attribute-specific feature vector
resides in the corresponding attribute-specific embedding space. If there are
$n$ attributes, $n$ attribute-specific embedding spaces are learned jointly.

Figure 1 illustrates the structure of our proposed network. We first improve
the CNN backbone to extract hierarchical features. The extracted hierarchical
feature representation is then passed into branches 1 and 2, respectively.
Branch 1 forces the model to recognize different attributes by making an
attribute classification, while branch 2 aims to learn multiple attribute-
specific embedding spaces for measuring fine-grained similarity. In branch 2,
it contains a multi-level attention module and a masked embedding module. In
what follows, we first detail the hierarchical feature extraction and branch
1, followed by the main body in branch 2, including the description of the
multi-level attention module and masked embedding module. Finally, the model
learning process is given.

Figure 1: The architecture of the proposed model. Figure 2: The masking
operation selects relevant embedding dimensions, given an attribute index.
Masking can be seen as a soft gating function to attend to a particular
attribute. Figure 3: Top-10 retrieval examples when cloth and attribute are
given on the FashionAI dataset Figure 4: Top-10 retrieval examples when cloth
and attribute are given on the DeepFashion dataset.

###  3.2 Hierarchical feature extraction and branch 1

To represent the image, we employ a CNN model pre-trained on ImageNet [4] as a
backbone network, e.g., ResNet50 [11]. In branch 1, to keep more of the low-
level information, we first add the output of block 2 and block 3 in ResNet50,
the addition result is then passed into block 4 of ResNet50 to extract the
hierarchical feature representation for the attribute classification. Note
that, each image has one label. Different from the feature that passed into
branch 1, to keep the multi-level spatial feature of the image, we concatenate
the addition result and block 3 extracted feature as the input feature
representation of branch 2. So the image is represented by
$I\in\mathbb{R}^{c\times h\times w}$, where $h\times w$ is the size of the
feature map, and $c$ indicates the number of channels.

###  3.3 Multi-level attention

In branch 2, our multi-level attention module contains three parts, named
attribute-aware spatial attention (ASA), spatial attention (SA), and
attribute-aware channel attention (ACA), respectively. For the attribute, we
represent it with a one-hot vector $a\in\\{0,1\\}^{n}$, where $n\in N$
indicates the number of different attributes.

ASA Fine-grained fashion retrieval needs to learn the feature representations
of certain related regions. For instance, in order to extract the attribute-
specific feature of the neckline design attribute, the region around the neck
is much more important than the others. Besides, as fashion images always show
up in large variations, e.g., various poses and scales, using a fixed region
with respect to a specific attribute for all images is not optimal. Hence, we
propose attribute-aware spatial attention that adaptively attends to certain
regions of the input image under the guidance of a specific attribute.
Specifically, we first transform the image representation
$I\in\mathbb{R}^{c\times h\times w}$ and the attribute to make their
dimensionality the same. For the image, we employ a convolutional layer
followed by batch normalization and a nonlinear tanh activation function.
Formally, the mapped image $p(I)\in\mathbb{R}^{c^{{}^{\prime}}\times h\times
w}$ is given by

$p(I)=\tanh(Conv_{c^{{}^{\prime}}}(I)),$ (1)

where $Conv_{c^{{}^{\prime}}}$ is a convolutional layer that contains
$c^{{}^{\prime}}$ $1\times 1$ convolution kernels.

For the attribute, we first project it into a $c^{{}^{\prime}}$-dimensional
vector through an attribute embedding, implemented by an embedding layer and a
fully connected (FC) layer, then perform self-attention. Hence, the mapped
attribute $p(a)\in\mathbb{R}^{c^{{}^{\prime}}\times h\times w}$ is

$p(a)=\sigma(W_{a}a)*W_{a}a,$ (2)

where $W_{a}\in\mathbb{R}^{c^{{}^{\prime}}\times n}$ denotes the
transformation matrix of the embedding and FC layers. $\sigma$ denotes a
sigmoid function. Finally, spatial attention is given by

$I_{s}=I*W_{1}s(p(I)\cdot p(a)),$ (3)

where $s$ indicates a softmax function and $W_{1}$ is a learned weight matrix.

SA Given a feature map $I_{s}$, the output feature maps of our SA layer
($I_{s^{{}^{\prime}}}$) could be computed as:

$I_{s^{{}^{\prime}}}=\sigma(Conv_{1}([Avg(I_{s}),Max(I_{s})]))*I_{s},$ (4)

where $Conv_{1}$ is a convolutional layer that only contains one $1\times 1$
convolution kernel, $Avg(I_{s})$ and $Max(I_{s})$ are the output of the
average channel pooling layer and max channel pooling layer.

ACA Although ASA and SA modules adaptively focus on the specific regions in
the image, the same regions may still be related to multiple attributes. For
example, attributes of collar design and collar color are all associated with
the region around the collar. Hence, we further employ attribute-aware channel
attention over the spatially attended feature vector $I_{s^{{}^{\prime}}}$.
The attribute-aware channel attention is designed as an element-wise gating
function which selects the relevant dimensions of the spatially attended
feature with respect to the given attribute. Concretely, we first employ an
embedding layer and an FC layer to embed an attribute into an embedding vector
with the same dimensionality of $I_{s^{{}^{\prime}}}$, that is:

$q(a)=\sigma(W_{a^{{}^{\prime}}}a)*(W_{a^{{}^{\prime}}}a),$ (5)

where $W_{a^{{}^{\prime}}}\in\mathbb{R}^{c^{{}^{\prime}}\times n}$ denotes
transformation matrix of the embedding and FC layers. Then the attribute
$q(a)$ and the spatially attended feature $I_{s^{{}^{\prime}}}$ are fused by
simple concatenation and further fed into the subsequent two FC layers to
obtain the attribute-aware channel attention weights. The final output becomes

$I_{c}=I_{s^{{}^{\prime}}}*W_{2}\sigma(W_{c_{2}}(r(W_{c_{1}}([q(a),I_{s^{{}^{\prime}}}])))),$
(6)

where $W_{c_{1}}\in\mathbb{R}^{c^{{}^{\prime}}\times n}$ and
$W_{c_{2}}\in\mathbb{R}^{c^{{}^{\prime}}\times n}$ denote two transformation
matrix of two FC layers, respectively. $[,]$ denotes a concatenation
operation, $r$ is a ReLU function, and $W_{2}$ is a learned weight matrix.

###  3.4 Masked embedding

After obtaining $I_{c}$, we introduce masks $m$ over the embedding with
$m\in\mathbb{R}^{c^{{}^{\prime}}\times n}$. We define a set of parameters
$\beta$ of the same dimension as $m$ such that $m=r(\beta)$, with $r$ denoting
a ReLU so that $r(\beta)=\max\\{0,\beta\\}$. As such, we denote $m_{a}$ to be
the selection of the $a$-th mask column of dimension $c^{{}^{\prime}}$ (in
pseudocode $m_{a}=m[:,a]$). The mask plays the role of an element-wise gating
function selecting the relevant dimensions of the embedding required to attend
to a particular concept. The role of the masking operation is visually
sketched in Figure 2. Thus the masked representations of the triplet input
${(I,I^{p},I^{n}|a)}$ are $I_{c}\times m_{a}$, $I_{c}^{p}\times m_{a}$, and
$I_{c}^{n}\times m_{a}$, denoted as ${(I_{m},I_{m}^{p},I_{m}^{n}|a)}$.

###  3.5 Model Learning

In branch 1, we don’t consider multi-label issues. Each image is assigned one
label. The attribute classification loss is computed with

$L_{c}=-w_{a}[y_{a}\cdot\log\sigma(x_{a})+(1-y_{a})\cdot\log(1-\sigma(x_{a}))].$
(7)

In branch 2, we require the distance of the masked embedding for images with
the same specific attribute value is smaller than those for images with
different ones. Concretely, the triplet ranking loss is defined as

$L_{triplet}\\!=\\!\max\\{0,m\\!-\\!D(I_{m},I_{m}^{p})\\!+\\!D(I_{m},I_{m}^{n})\\},$
(8)

where $m$ represents the margin, empirically set to be 0.2,
$D(I_{m},I_{m}^{p})$ denotes the cosine similarity between $f(I,a)$ and
$f(I^{p},a)$.

We calculate a weighted loss of the attribute classification loss and the
triplet ranking loss. Thus the final loss is given by

$L=w_{0}L_{c}+w_{1}L_{triplet},$ (9)

where $w_{0-1}$ are learned weight parameters.

##  4 Experimental results

###  4.1 Experimental settings

Figure 5: Visualization of the attribute-aware spatial attention with the
guidance of a specified attribute (above the attention image) on FashionAI.
Figure 6: Visualization of the attribute-aware spatial attention with the
guidance of a specified attribute (above the attention image) on DeepFashion.

To verify the viability of the proposed model for fine-grained fashion
similarity computation, we evaluate it on the following two tasks. (1)
Attribute-specific fashion retrieval: Given a fashion image and a specified
attribute, its goal is to search for fashion images of the same attribute
value as the given image. (2) Triplet relation prediction: Given a triplet of
$\\{I,I^{{}^{\prime}},I^{{}^{\prime\prime}}\\}$ and a specified attribute, the
task is asked to predict whether the relevance between $I$ and
$I^{{}^{\prime}}$ is larger than that between $I$ and $I^{{}^{\prime\prime}}$
in terms of the given attribute. For task (1), FashionAI [20], and DeepFashion
[19] datasets are adopted. We report the Mean Average Precision (MAP), a
popular performance metric in many retrieval-related tasks [20]. For task (2),
Zappos50k dataset [37] is used. We utilize prediction accuracy as the metric.
Note that, branch 1 of our model is removed when evaluating this task to
evaluate the effectiveness of our multi-level attention module compared with
the attention modules proposed in other methods. Details of all the used
datasets are given in the supplementary material.

###  4.2 Results

####  4.2.1 Attribute-specific fashion retrieval

Tables 1 and 2 summarize the performance of different models on the FashionAI
and DeepFashion datasets respectively. The performance of the compared models
on each attribute type is reported. As we can see, our model achieves better
performance for all attribute types than the state-of-the-art methods. Note
that, for design-related attributes, our model consistently outperforms the
other counterparts. Even for the more complicated length-related attributes,
our model shows a big advantage compared with existing methods.

Table 1: Performance of attribute-specific fashion retrieval on FashionAI. Our
model consistently outperforms the other counterparts for all attribute types.

Methods | MAP for each attribute (Fashion AI) | MAP $\uparrow$
---|---|---
Skirt length | Sleeve length | Coat length | Pant length | Collar design | Lapel design | Neckline design | Neck design
CSN [30] | 59.5852 | 43.9943 | 42.8203 | 62.8021 | 67.5715 | 48.1033 | 41.7291 | 55.2886 | 50.8043
ASENet_V2 [20] | 64.5731 | 54.9630 | 51.7645 | 64.4973 | 71.9256 | 66.7201 | 60.2929 | 60.8307 | 60.7551
AttnFashion [31] | 61.9057 | 45.8526 | 45.8116 | 60.6013 | 70.4638 | 62.7362 | 52.0484 | 59.5861 | 55.3646
ISLN [36] | 65.3194 | 55.6850 | 52.6229 | 66.4398 | 71.4715 | 66.6788 | 61.1109 | 61.1696 | 61.4612
Our | 65.1933 | 56.4381 | 53.9879 | 70.3412 | 72.2916 | 67.3052 | 63.4835 | 62.6355 | 62.9361

Figures 3 and 4 present the top-10 retrieval examples when cloth and attribute
are given on the FashionAI and DeepFashion datasets, respectively. Note that
the FashionAI dataset contains complex backgrounds and the attributes are all
about local regions. While the DeepFashion dataset contains a clean background
and most of the attributes are about global regions. As we can see, most of
the top-10 retrieval examples are in accordance with human perception. It
demonstrates the effectiveness of the proposed AG-MLAN model.

Table 2: Performance of attribute-specific fashion retrieval on DeepFashion.
our model performs best.

Methods | MAP for each attribute (DeepFashion) | MAP $\uparrow$
---|---|---
Texture-related | Fabric-related | Shape-related | Pant-related | Style-related
CSN [30] | 14.4546 | 6.5021 | 11.2555 | 4.7751 | 3.4673 | 8.0747
ASENet_V2 [20] | 15.5168 | 7.1855 | 11.5066 | 5.5220 | 3.6514 | 8.6704
AttnFashion [31] | 11.8423 | 5.7248 | 8.6115 | 4.0904 | 3.2329 | 6.8060
ISLN [36] | 14.9985 | 7.1098 | 11.7784 | 5.4627 | 3.6797 | 8.6033
Our | 15.5193 | 7.1582 | 12.2916 | 5.6928 | 3.7986 | 8.8754

Table 3: Performance of triplet relation prediction on Zappos50k. Ours is the
best

Methods | Zappos50k
---|---
Average loss $\downarrow$ | Prediction Accuracy(%) $\uparrow$
CSN [30] | 0.0393 | 93.14
ASENet_V2 [20] | 0.0430 | 92.54
AttnFashion [31] | 0.0664 | 91.37
ISLN [36] | 0.0426 | 92.59
Our | 0.0390 | 93.32

To gain further insight into our proposed network, we visualize the learned
attribute-aware spatial attention. As shown in Figures 5 and 6, the learned
attention map gives relatively high responses on the relevant regions while
low responses on irrelevant regions with the specified attribute, showing the
attention is able to figure out which regions are more important for a
specific attribute. Compared with the state-of-the-art methods, our model can
better locate the related regions for both design-related and length-related
attributes, which further demonstrates the effectiveness of attribute-aware
spatial attention. Moreover, attention maps for length-related attributes are
more complicated than that for design-related attributes; multiple regions
show a high response for the former. However, our model can better locate the
start and end of a fashion item to speculate its length.

####  4.2.2 Triplet relation prediction

Table 3 shows the results on the Zappos50k dataset. Among the five embedding
learning models, our proposed model outperforms other methods. As we already
removed branch 1 of our AG-MLAN model, the result verifies the effectiveness
of our multi-level attention module for triplet relation prediction compared
with other attention methods. Some visualization results are given in the
supplementary material.

##  5 Conclusions

To solve the problem of fine-grained similarity retrieval in the fashion
field, this paper proposes an attribute-guided multi-level attention network
(AG-MLAN) to learn fine-grained fashion similarity. AG-MLAN is able to make a
more accurate attribute positioning and capture more discriminative features
under the guidance of the specified attribute. Concretely, we first improve
the Resnet50 to extract hierarchical feature representations. The extracted
features are then passed into two branches, where branch 1 forces the model to
recognize different attributes by making an attribute classification, while
branch 2 aims to learn multiple attribute-specific embedding spaces for
measuring the fine-grained similarity. In branch 2, we first propose a multi-
level attention module to extract a more discriminative attribute-specific
representation. The extracted representation is then passed into a masked
embedding module to further improve its positioning ability. Finally, the AG-
MLAN is trained with a weighted loss of the classification loss in branch 1
and the triplet loss of the masked embedding features in branch 2 to improve
the accuracy in attribute location. Extensive experiments on the DeepFashion,
FashionAI, and Zappos50k datasets show the higher performance of our AG-MLAN
for fine-grained fashion similarity learning compared with the state-of-the-
art methods. The proposed AG-MLAN has a high potential to be adopted in other
attribute-guided recognition tasks.

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