11institutetext:  Humatics s.r.l
11email<EMAIL_ADDRESS>
22institutetext: University of Verona
22email<EMAIL_ADDRESS>
# POP: Mining POtential Performance of new fashion products via webly cross-
modal query expansion

Christian Joppi indicates equal contribution.11 0000-0003-4495-9515 Geri
Skenderi00footnotemark: 0 22 0000-0001-9968-7727 Marco Cristani 2211
0000-0002-0523-6042

###### Abstract

We propose a data-centric pipeline able to generate exogenous observation data
for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e.,
predicting the performance of a brand-new clothing probe with no available
past observations. Our pipeline manufactures the missing past starting from a
single, available image of the clothing probe. It starts by expanding textual
tags associated with the image, querying related fashionable or unfashionable
images uploaded on the web at a specific time in the past. A binary classifier
is robustly trained on these web images by confident learning, to learn what
was fashionable in the past and how much the probe image conforms to this
notion of fashionability. This compliance produces the POtential Performance
(POP) time series, indicating how performing the probe could have been if it
were available earlier. POP proves to be highly predictive for the probe’s
future performance, ameliorating the sales forecasts of all state-of-the-art
models on the recent VISUELLE fast-fashion dataset. We also show that POP
reflects the ground-truth popularity of new styles (ensembles of clothing
items) on the Fashion Forward benchmark, demonstrating that our webly-learned
signal is a truthful expression of popularity, accessible by everyone and
generalizable to any time of analysis. Forecasting code, data and the POP time
series are available at: https://github.com/HumaticsLAB/POP-Mining-POtential-
Performance

###### Keywords:

Computer Vision for Fashion, Data-centric Artificial Intelligence, Time Series
Forecasting

##  1 Introduction

Forecasting the performance of a new clothing item is a crucial challenge for
fashion companies [7, 11]. A good forecast in terms of predicted sales or
product popularity can greatly help optimize the supply chain [31] and
minimize losses on multiple levels. Unfortunately, standard forecasting
approaches require observations from the past to provide a forecast for the
same product in the future [14, 1] and this information is typically available
for evergreen products only (Fig. 1a). In other cases, judgemental forecasts
[14] from fashion professionals111A commercial example is Trendstop
https://www.trendstop.com/ and its “Trend Platform Membership” service are the
only ones that can help. Starting from photos or realistic renderings, which
we call _probe_ images, they perform comparisons with trends as they surface
and then infer the probe’s success [31].

Figure 1: a) A standard forecasting setup, where an evergreen item has past
observations to exploit, e.g., # sales; b) New Fashion Product Performance
Forecasting (NFPPF) problem, where no past observations are available and
exogenous data must be considered. Here we propose POP, the POtential
Performance series, which is webly learned. The signals in b) appear on the
same scale purely for visualization purposes, in reality this might not be the
case.

In this paper, we try to model this line of reasoning and create a data-
centric [26] pipeline that is able to extract a highly predictive signal from
the web, dubbed “POtential Performance” (POP), which can be fed into any NFFPF
forecasting model as an additional variable and lead to more accurate
forecasts. (see Fig. 1b).

Our cross-modal, query expansion based pipeline is sketched in Fig. 2. The
input is a single probe image of the product to be analyzed, or a
photorealistic rendering222Several such tools are available, for instance
https://www.tg3ds.com/3d-fashion-design-tools.. The pipeline first extracts
textual tags from the probe automatically or by directly considering the
associated technical sheet. The tag set is expanded with _positive_ and
_negative_ tags that are used to perform a _time-dependent_ query online,
i.e., collecting images of “fashionable” and “unfashionable” items related to
the tags, which have been uploaded during some specified $K_{past}$ intervals
in the past. These images are used to _confidently learn_ [28] a binary
classifier that captures what is fashionable VS unfashionable in that
interval. This learning procedure prunes noisy images from both the positive
and negative classes, resulting in a robust model. Subsequently, pruned
positive images are projected into an embedding space by the learned model and
compared with the (also projected) initial probe image, providing the
$K_{past}$-long POP signal. The POP signal indicates how popular the probe
could have been over time if it were available earlier in the past.

Our approach should be cast in the field of _data-centric artificial
intelligence_ (DCAI) [26], since it automates the creation of high quality
training data that can be used to improve any forecasting model which
accommodates multivariate time series forecasting. POP has been tested on
diverse state-of-the-art NFPPF algorithms that predict sales curves of new
products on the recent VISUELLE fast-fashion dataset [35], providing superior
performance when compared to other types of training signals. It has also been
customized to deal with fashion styles (i.e., ensembles of clothing items) on
the Fashion Forward benchmark [1]. Fashion Forward (FF) calculates a
popularity time series for an automatically extracted style based on the
dataset properties and then applies standard forecasting algorithms. We
substitute their popularity series with POP, reaching similar predictions
despite relying only on an exogenous input. Surprisingly, on the Dresses
partition of FF, we reach the absolute best, suggesting that POP can foresee
the success of a potentially new fashion style. Summarizing, the contributions
of this work are threefold:

  1. 1.

The first data-centric strategy tailored to forecasting, used to create an
exogenous observation signal which improves forecasts of the performance
(number of sold items, popularity) of brand new clothing items with non-
existent pasts.

  2. 2.

A webly-learned method to freely collect information about fashion trends
without relying on private or costly repositories.

  3. 3.

Best overall results on all the tested NFPPF tasks.

The rest of the paper is organized as follows: related literature is analyzed
in Sec. 2; the proposed approach is detailed in Sec. 3; experiments are
reported in Sec. 4, and finally; concluding remarks are drawn in Sec. 5.

##  2 Related literature

NFPPF problem. The NFPPF problem has been deeply investigated in the fields of
quantitative fashion design [30, 3, 16], marketing and social sciences [33,
12], but is relatively new in the computer vision community. In both [9, 34],
the main idea is that new products will sell comparably to similar, older
products; this similarity is exploited in [34] via textual tags only, while in
[9] an autoregressive RNN model takes past sales, textual product attributes,
and the product image as input, to forecast the item sales. The work in [35]
focuses on the additional direction of checking the past to look for
predictive exogenous signals. In particular, the authors exploit Google
Trends, querying textual attributes related to the probe and embed the
resulting trend into a Transformer-based [38] architecture, which considers
images, text and other metadata. The authors also rendered accessible the
first publicly available dataset for NFPPF, VISUELLE. In our paper we follow
the idea of looking back to web data, but use images as the main
representation of online fashionability, obtaining a richer exogenous signal.
Predicting the success of new fashion styles has never been taken into
account, with past works [1, 20, 22] focusing on the standard forecasting
setup.

Data-centric AI. Data-Centric AI [26] (DCAI) shifts the attention from the
models to the data used to train and evaluate them. It is a topic whose
importance is constantly growing in many AI communities [2, 29,
27]333https://datacentricai.org/., with important effects on CV & ML. In
general, DCAI investigates methodologies for accelerating open-source dataset
creation from lower-quality resources. Consequently, it is tightly coupled
with learning on noisy data, which aims at producing consistent and low noise
data samples, or removing labeling noise and inconsistencies from existing
data [28, 36, 39]. Our methodology is data-centric, since it automates the
creation of training data from a large amount of web resources, while removing
labeling noise. Notably, it represents a novelty in the DCAI panorama, since
it creates _temporally-dependent_ training data, i.e., time series, as it is
required by NFPPF and in general by forecasting tasks.

##  3 Methodology

The goal of our approach is to produce an exogenous variable that can aid a
forecasting model in predicting the future performance of a product (sales,
popularity). The input to our approach is the probe image $\mathbf{z}^{(t)}$,
where $\mathbf{z}$ represents the new clothing item and $t$ the _observation
time_ , which is the date from when we begin to look into the past. The output
is the POP signal
$S^{(t)}_{\mathbf{z}}=s^{(t-K_{past})}_{\mathbf{z}},\ldots,s^{(t-k)}_{\mathbf{z}},\ldots,s^{(t-1)}_{\mathbf{z}}$,
defined for $K_{past}$ time steps preceding $t$, where $k=1,\ldots,K_{past}$
and $s^{(t-k)}_{\mathbf{z}}\in\mathbb{R}$. In this paper, we describe the
observation times in terms of weeks and set $K_{past}=52$. This translates to
looking one year prior to the observation time $t$, as typically done in
fashion market analysis [37]. The next sections will sequentially detail the
general pipeline of our approach, depicted in Fig. 2.

Figure 2: Schematic pipeline of our approach; we start with a probe image and
obtain the POtential Performance (POP) signal at the end. Along this pipeline,
we sequentially process information in different modalities, thereby creating
a cross-modal signal.

###  3.1 Image Tagging

The first operation is the extraction of textual tags
$\\{a_{\mathbf{z}}^{(j)}\\}_{j=1,\ldots,J}$ associated to $\mathbf{z}$. These
tags should represent the clothing item with sufficient generality, capturing
at least categorical information (e.g “long sleeve”) and a dominant color (e.g
“yellow”). Empirically, we found these tags to work well while being easily
obtainable. Category and color can be automatically extracted with high
accuracy [19] or are usually contained in the technical data sheet
accompanying the product, which is what we exploit in this work, as shown in
Sec. 4.

Figure 3: Pipeline insights on _Time-dependent Query Expansion_ (Sec. 3.2),
_Image Web Search_ (Sec. 3.3) and _Learning From Noisy Labels_ (Sec. 3.4)
steps. This figure reports a real world excerpt of the download and processing
of $N$=2600 images ($N=2(M\times K_{past})$, $M$ = 25, $K_{past}$=52).

###  3.2 Time-dependent Query Expansion

The second operation (detailed in Fig. 3 on a real example) performs two
different textual query expansions, generating _positive expansions_ ,
${\\{a_{\mathbf{z}}^{(j)}\\}}_{j=1,\ldots,J}\cup J^{(+)}$ where the additional
$J^{(+)}$ tags indicate attractive clothing items, and conversely for
_negative expansions_. In this paper, we found the tags $J^{(+)}=$
“fashionable” and $J^{(-)}=$ “unfashionable” to be the most effective for
positive and negative expansions, respectively. Alternatives as “best seller”
and “unattractive” were considered, returning similar results.

Each expansion, either positive or negative, is associated to a particular
$k=1,...,K_{past}$ for the time interval $[t-k-W,t-k]$, where $W$ is a
temporal window we wish to consider for the image search, also expressed in
weeks. In our experiments we set $W=4$, which translates to having a sliding
window of size 4 and stride of 1 over the temporal axis. This allows the pool
of downloaded images to disclose what are newly indexed items in relation to
previous time steps, developing a temporal locality in the data pool. The
precise value of $W$ was chosen after an empirical evaluation over the range
$1,...,12$.

###  3.3 Image Web Search

A given expanded textual query along with a time interval is fed into a web
API request to gather $M$ representative fashionable and unfashionable images
${}^{(+)}\\{\mathbf{x}_{i}\\}_{i=1,\ldots,M}^{(t-k)};{}^{(-)}\\{\mathbf{x}_{i}\\}_{i=1,\ldots,M}^{(t-k)}$
that have been uploaded in the interval $[t-k-W,t-k]$, for
$k=1,\ldots,K_{past}$. In particular, we adopt Google Image search, selecting
the first $M=25$ images returned, assuming the ordering of Google Images
perfectly mirrors a genuine image relevance [17]. After the image web search
phase, $M\times K_{past}$ fashionable and unfashionable images are collected
respectively (as shown in Fig. 3). These images are then used to train a
binary classifier $\theta$, aimed at distinguishing fashionable from
unfashionable images. Webly learning and supervision based on Google Images
has been considered before in computer vision, especially for image
classification and object detection [10, 6, 18]. POP goes one step further,
merging visual and textual search while adding a time-dependent query
expansion to create more discriminative image sets. Nevertheless, the labels
assigned to the images from the query expansions might be noisy, therefore we
apply a confident learning method.

###  3.4 Learning From Noisy Labels

In the following, we adapt the confident learning (CL) methodology
specifically for our binary problem. For a broader overview, readers may refer
to [28]. Let $\mathbf{X}=\\{\mathbf{x}_{i},\tilde{y_{i}}\\}_{1\ldots N}$ be
our set of $N=2(M\times K_{past})$ images with associated observed noisy
binary labels
$\tilde{y_{i}}\in\\{\text{``fashionable''},\text{``unfashionable''}\\}$. CL
assumes that a true, latent label
$y^{*}_{i}\in\\{\text{``fashionable''},\text{``unfashionable''}\\}$ exists for
every sample. CL requires two inputs: 1) the out-of-sample $N\times 2$ matrix
$\mathbf{\hat{P}}$ of predicted probabilities where
$\mathbf{\hat{P}}_{i,h}=\hat{p}(\tilde{y_{i}}=h;\mathbf{x}_{i},\mathbf{\theta})$
with $\mathbf{\theta}$ a generic (binary) classifier initially trained on
$\mathbf{X}$; 2) the set of noisy labels $\\{\tilde{y}_{i}\\}$. Subsequently,
a robust $2\times 2$ confusion matrix, called the _confident joint_ matrix
$\mathbf{C}_{\tilde{y},y^{*}}$, is computed444We drop the index $i$ for
clarity.:

$\begin{split}\mathbf{C}_{\tilde{y},y^{*}}(h,l)&=|\mathbf{\hat{X}}_{\tilde{y}=h,y^{*}=l}|,\text{with}\\\
\mathbf{\hat{X}}_{\tilde{y}=h,y^{*}=l}&=\Bigg{\\{}\mathbf{x}\in\mathbf{X}_{\tilde{y}=h}:\hat{p}(\tilde{y}=l;\mathbf{x},\mathbf{\theta})\geq
t_{l}\Bigg{\\}}\end{split}$ (1)

where $t_{l}$ is a threshold that represents the expected self confidence
value for each class:

$t_{l}=\frac{1}{|\mathbf{X}_{\tilde{y}=l}|}\sum_{x\in\mathbf{X}_{\tilde{y}=l}}\hat{p}(\tilde{y}=l;x,\mathbf{\theta})$
(2)

In practice, $\mathbf{C}_{\tilde{y},y^{*}}$ counts only those elements which
have been confidently classified in a particular class, where the term
“confident” means with a probability that is higher than the average
probability of an element belonging to that class. In simpler words, if
samples labeled as belonging to class $h$ tend to have higher probabilities
because the model is over-confident about class $h$, then $t_{h}$ will be
proportionally larger. It also worth noting that Eq. 1 corresponds to a
simplified version of the general building procedure of the confident joint
matrix $\mathbf{C}_{\tilde{y},y^{*}}$ of [28], which nonetheless in our case
is perfectly acceptable since we deal with binary classification and no _label
collision_ may happen, i.e., the fact that a noisy label can correspond to a
more than a single alternative class.

On this robust confusion matrix, we estimate label errors from the off
diagonal elements of $\mathbf{C}_{\tilde{y},y^{*}}(h,l)$. Wrongly labeled
images are therefore pruned (indicated by the red boxes in Fig. 3), obtaining
the cleaned fashionable and unfashionable images
${}^{(+)}\\{x^{\prime}_{i}\\}_{i=1,\ldots,M^{\prime(t-k)}}^{(t-k)};{}^{(-)}\\{x^{\prime}_{i}\\}_{i=1,\ldots,M^{\prime\prime(t-k)}}^{(t-k)}$,
where $M^{\prime(t-k)}$ and $M^{\prime\prime(t-k)}$ indicate that we can have
a different number of positive and negative images, respectively, related to
each $t-k$ time step, due to the noisy sample elimination. The classifier is
retrained on the cleaned data, obtaining a robust trained model
$\theta^{\prime}$. This procedure is data-centric and model agnostic; the
specific $\theta$ used in this work is described in Sec. 4.

###  3.5 Signal Forming

The POP signal
$S^{(t)}_{\mathbf{z}}=s^{(t-K_{past})}_{\mathbf{z}},\ldots,s^{(t-k)}_{\mathbf{z}},\ldots,s^{(t-1)}_{\mathbf{z}}$,
is computed by considering the cleaned fashionable images
${}^{(+)}\\{\mathbf{x}^{\prime}_{i}\\}_{i=1,\ldots,M^{\prime(t-k)}}^{(t-k)}$,
the robust model $\theta^{\prime}$, and the image $\mathbf{z}$, as follows:

$\small
s^{(t-k)}_{\mathbf{z}}=\frac{1}{M^{\prime(t-k)}}\sum_{i=1}^{M^{\prime(t-k)}}\frac{\langle\theta^{\prime}\left({}^{(+)}{\mathbf{x}^{\prime}_{i}}^{(t-k)}\right)\cdot\theta^{\prime}\left(\mathbf{z}\right)\rangle}{{\parallel\theta^{\prime}\left({}^{(+)}{\mathbf{x}^{\prime}_{i}}^{(t-k)}\right)\parallel}{\parallel\theta^{\prime}\left(\mathbf{z}\right)\parallel}}$
(3)

where $\theta^{\prime}\left(\mathbf{z}\right)$ indicates the extracted
features of $\mathbf{z}$ from $\theta^{\prime}$, and $\langle\cdot\rangle$
indicates the dot product. In other words, the signal value
$s^{(t-k)}_{\mathbf{z}}$ is the average cosine similarity between the
embedding of the probe image $\mathbf{z}$ and each fashionable image
$\mathbf{x}^{\prime}_{i^{(t-k)}}$ from the $M^{\prime(t-k)}$ downloaded
images. An assessment of alternative signal forming options is shown in the
supplementary material.

##  4 Experiments

In line with the general requirements of DCAI [26], we show how our
automatically manufactured time series helps a forecasting model $\psi$
achieve better results on a given task $\gamma$. The main idea behind our
approach is that by knowing POP, the forecasting model can gain a context on
the past which otherwise would be missing and therefore improve. To
demonstrate this, we perform extensive evaluation on two tasks (and different
forecasting models): _new fashion product sales curve prediction_ [34, 9, 35],
and _style popularity forecasting_ [1]. We show ablative studies on the first
task and an impressive outcome on the second.

The binary classifier $\theta$ for learning on noisy data (Sec. 3.4) is based
on a ResNet50 [13], pre-trained on ImageNet [8], with two additional fully
connected layers. During the confident learning procedure, we fine-tune its
last convolutional block and fully connected layers for 50 epochs with a batch
size of 64, using CE loss, following a 5-fold cross validation protocol. AdamW
[21] is used as optimizer, with a learning rate of $1e-4$. The forecasting
neural network models are all trained for 200 epochs with a batch size of 128
and L2 loss, using the AdaFactor [32] optimizer. The experiments are performed
on two NVIDIA 3090 RTX GPUs.

###  4.1 Task 1: New Fashion Product Sales Curve Prediction

The output of a sales curve forecasting model for a probe clothing item
$\mathbf{z}$ is a time series
$O^{(st)}_{\mathbf{z}}=o^{(st+1)}_{\mathbf{z}},\ldots,o^{(st+k)}_{\mathbf{z}},\ldots,o^{(st+K_{fut})}_{\mathbf{z}}$
that indicates how many pieces of $z$ will be sold starting at a particular
time step $st$ (typically the start of the season), for the next $K_{fut}$
time steps.

We run our first set of experiments on the VISUELLE dataset [35]. For each
available product, multi-modal information is provided: i) images, ii) text
tags, iii) Google Trends, iv) sales curves. The evaluation protocol follows
that of VISUELLE, simulating how a fast-fashion company deals with new
products on two particular moments: the _first order setup_ and the _release
setup_. The former takes place when the company decides which products and how
many pieces to order by looking at probe images. The latter is right before
the season, and is useful to obtain an accurate forecast in order to plan
stock replenishment. These two setups use 28 and 52 week long exogenous
signals (originally Google Trends [35]), respectively.

Note that for the sake of fairness, we do not alter the training setup or
models from [35], keeping the cardinality and the type of the training data
fixed and _substituting only the Google Trends with our POP signal_. All the
models are trained considering the 12-week long sales signals, whilst the
evaluation is done on a 6-week horizon. This is shown to give the best
predictions while simulating politics of real fashion companies [35].

Figure 4: Forecasting WAPE results per clothing category; the larger the blob,
the higher the # of items in that category; the color below each category name
indicates the type of training setup which gives the best WAPE.

We consider 5 algorithms (from oldest to newest): _Gradient Boosting_ for
forecasting [15], Concat Multi-Modal RNN [9] (_Concat MM RNN_ in the tables),
Residual Multi-Modal RNN [9] (_Residual MM RNN_), Cross-Attention RNN [9] (_X-
Attention RNN_) and GTM Transformer [35] (_GTM Transf._) We consider the
Weighted Absolute Percentage Error (WAPE) as primary evaluation metric and
also compute the Mean Absolute Error (MAE) to demonstrate the error on an
absolute scale [35]:

Note that the WAPE is not bounded by 100. Finally, we measure the similarity
of the slope of the predicted curve with the ground truth using the _Edit
distance with Real Penalty_ (ERP) [5]. This metric counts the number of edit
operations (insert, delete, replace) that are necessary to transform one
series into the other. Because we are dealing with continuous values, a
threshold $\epsilon$=0.03 is used to decide if values are considered different
and have to be edited.

Table 1: Results on VISUELLE with the _first order setup_ ; “W” stands for
WAPE, “M” for MAE. Lower is better for all metrics.

_First Order Setup ( $K_{best}$ = 28 weeks)_
---
Exogenous | _Gradient_ | _Concat_ | _Residual_ | _X-Attention_ | _GTM_
Signal | _Boosting_ | _MM RNN_ | _MM RNN_ | _RNN_ | _Transformer_
|  [15] 2020 |  [9] 2020 |  [9] 2020 |  [9] 2020 |  [35] 2021
| W | M | ERP | W | M | ERP | W | M | ERP | W | M | ERP | W | M | ERP
_ No Signal _ | 64.10 | 35.02 | 0.43 | 63.31 | 34.41 | 0.42 | 64.26 | 34.92 | 0.44 | 59.49 | 32.33 | 0.38 | 56.62 | 30.93 | 0.37
Google Trends | 64.29 | 35.12 | 0.43 | 64.11 | 34.84 | 0.43 | 68.11 | 37.02 | 0.47 | 58.70 | 31.90 | 0.38 | 56.83 | 31.05 | 0.35
POP Signal | 63.75 | 34.83 | 0.42 | 58.09 | 31.73 | 0.39 | 58.88 | 32.16 | 0.39 | 57.78 | 31.56 | 0.38 | 53.41 | 29.18 | 0.32

Table 2: Results on VISUELLE with the _release setup_ ; “W” stands for WAPE,
“M” for MAE. Lower is better for all metrics.

_Release Setup ( $K_{best}$ = 52 weeks)_
---
Exogenous | _Gradient_ | _Concat_ | _Residual_ | _X-Attention_ | _GTM_
Signal | _Boosting_ | _MM RNN_ | _MM RNN_ | _RNN_ | _Transformer_
|  [15] 2020 |  [9] 2020 |  [9] 2020 |  [9] 2020 |  [35] 2021
| W | M | ERP | W | M | ERP | W | M | ERP | W | M | ERP | W | M | ERP
_ No Signal _ | 64.10 | 35.02 | 0.43 | 63.31 | 34.41 | 0.42 | 64.26 | 34.92 | 0.44 | 59.49 | 32.33 | 0.38 | 56.62 | 30.93 | 0.37
Google Trends | 63.52 | 34.70 | 0.42 | 65.87 | 35.80 | 0.44 | 68.46 | 37.21 | 0.48 | 59.02 | 32.08 | 0.38 | 55.24 | 30.18 | 0.33
POP Signal | 63.38 | 34.62 | 0.42 | 57.43 | 31.37 | 0.36 | 58.38 | 31.89 | 0.39 | 57.36 | 31.33 | 0.36 | 52.39 | 28.62 | 0.29

The results are shown in Table 1 for the _first order setup_ and in Table 2
for the _release setup_. As reference, we also report results _without_ any
exogenous series, to show the net value of these indicators. For all the
algorithms and both setups, adding POP to the model boosts the performances
over all the metrics, reaching the absolute best when coupled with GTM
Transformer. On average, in the _first order setup_ , we improve the WAPE by
3.42% over the Google Trends and by 3.21% over not using any exogenous
signals. In the _release setup_ we improve by 2.85% over the Google Trends and
by 4.23% over not using exogenous signals. These results demonstrate how our
data-centric approach can provide optimal forecasts by creating a highly-
predictive signal of past popularity that is image-based, unlike Google
Trends. The forecasts are performed on 497 products over different stores,
meaning that these improvements can provide a large impact on the supply chain
operations.

In Fig. 4 we show the WAPE _per clothing category_. We mostly perform better
than the other training alternatives, yet some particular categories display
limitations of our approach. These limitations arise due to the fact that the
Image Tagging phase is assumed as flawless, since we rely on the technical
sheet accompanying the probe image to extract the tags. The results per
category (Fig. 4) display how possibly mislabeled categories, or categories
labeled in a general manner (“solid colours”,“doll dress”) may lead to
misleading web images. As visible in Fig. 5, the related images from the web,
both fashionable and not, are completely useless, since the tag of the
category itself is misleading. In such cases, a robust automated category
extraction could potentially lead to better results.

Figure 5: Examples of VISUELLE items (seasons SS17 and SS18 on the left, SS19
and AI19 on the right, respectively) and the correspondent
fashionable/unfashionable images from the web. Some web images can be
misleading, due to the questionable category names of the VISUELLE dataset
(“solid colours”, “doll dress”).

####  4.1.1 Ablation studies.

In the following, we focus on alternative versions of our proposed pipeline,
ablating the specific modules illustrated in Fig. 2. Table 3 contains all the
results.
Time dependent query expansion

  * •

_No expansion:_ Images are queried with the original tags collected in the
Image Tagging phase, without generating positive or negative expansions. This
is equivalent to querying only with ”color + category”. The learning step is
impacted directly, since no positive or negative classes are available for
learning, therefore we use our backbone model to extract image features. For
each image $\mathbf{z^{(t)}}$, the web images
$\\{\mathbf{x}_{i}\\}_{i=1,\ldots,M}^{(t-k)}$ that have been uploaded in the
interval $[t-k-W,t-k]$, for $k=1,\ldots,K_{past}$ are collected. The signal
forming Eq. 3 changes accordingly, using all the $M$ downloaded images;

  * •

_Misaligned past:_ We modify the query expansions by looking one year earlier
than the “correct” past. Given the observation time $t$ of the probe
$\mathbf{z}^{(t)}$, instead of looking backwards from $t-1$ weeks to
$t-K_{past}$, we go from $t-1-K_{past}$ to $t-2\cdot K_{past}$.

With respect to all the alternative versions in this study, the _No expansion_
ablation gives the worst result. POP provides an improvement of 0.73% and
1.06% WAPE for the _first order setup_ and _release setup_ , respectively. The
_Misaligned past_ yields slightly better results, but still performs worse
than POP by 0.63% and 0.22% WAPE for the _first order setup_ and _release
setup_ , respectively. This confirms that fashion has an evolution that
changes year after year that we have to take into account.

Table 3: Alternative versions of our pipeline (Fig. 2) on both the _release_
and _first order_ setups; “W” stands for WAPE, “M” for MAE. Lower is better
for all metrics.

_Time Dependent Query Expansion_
---
| _Release Setup_ | _First Order Setup_
Strategy | W | M | W | M
_No Expansion_ | 53.12 | 29.02 | 54.47 | 29.77
_Misaligned past_ | 53.02 | 28.96 | 53.63 | 29.30
_Learning With Noisy Labels_
| _Release Setup_ | _First Order Setup_
Strategy | W | M | W | M
_No Learning_ | 53.03 | 28.97 | 53.83 | 29.41
_No Robust Learning_ | 52.81 | 28.85 | 53.59 | 29.28
_Symmetric Cross Entropy_ [39] | 52.63 | 28.75 | 53.58 | 29.27
_SELFIE_ [36] | 52.56 | 28.71 | 53.51 | 29.23
POP | 52.39 | 28.62 | 53.41 | 29.18

Learning from noisy data

  * •

_No learning_ : A predefined image classification network is used to compute
the distance among embeddings of the probe image with the positive, downloaded
images. This is equivalent to ablating the “Learning from Noisy Data” phase of
Fig. 2. It will highlight the importance of dealing with distances among
embeddings which are specifically learned against distances coming from a
general purpose network. We utilise the backbone of our binary classifier,
specified in the introduction of Sec. 4;

  * •

_No robust learning_ : All of the downloaded positive and negative images are
used to learn our binary classifier without pruning noisy data by confident
learning;

  * •

_Symmetric cross entropy_ [39]: SCE is a robust classification loss; it adds
to the standard cross entropy loss a _reverse cross entropy_ term which
assumes the predicted labels as ground truth, and the original labels as
possibly faulty. In practice, it penalizes noisy labels, without removing any
associated training data;

  * •

_SELFIE_ [36]: the key idea is to correct the label of noisy _refurnishable_
samples with high precision, with the help of clean data which is defined as
those samples within a mini-batch creating a small loss. Repeated training
runs (dubbed “restarts”) allow to use more training data, i.e., noisy samples
which have been corrected in their labels. In particular, we use 3 restarts,
after which 1.1% of both fashionable and unfashionable items have been removed
from the training data.

The results in Table 3 show slightly different performances, promoting the
general idea of learning from webly data. _No learning_ gives the worse
performance, indicating that a fine tuning on the web data is beneficial
(53.03 and 53.83 WAPE); when learning is done on the web data, there is some
increase (52.81 and 53.59 WAPE); when learning is robust to label noise, with
SCE, performances are better (52.63 and 53.58 WAPE); removing some outliers
with SELFIE gives a further help (52.56 and 53.51 WAPE). Confident learning
remains the best solution, with 52.39 and 53.41 WAPE, while removing 0.8% and
1.1% of fashionable and unfashionable items respectively, from the 14,500,200
images mined using our cross-modal pipeline.

Figure 6:  Qualitative results for the sales forecast of two different
products on VISUELLE, considering all 12 time-steps. In all cases, using POP
provides better forecasts. In the bottom row (bottom right plot), we show a
forecasting failure case, where the product is discounted in its final week of
sales.

###  4.2 Task 2: Popularity Prediction Of Fashion Styles

The style popularity prediction task [1] is different from product sales
forecasting in that it considers a popularity signal $y$ based on multiple
clothing items. In the literature [1, 23], style is defined as a latent
property of a set of clothing images that share some common visual features.
Concretely, in Fashion Forward (FF) [1], Non-negative Matrix factorization is
applied to extract $K$ styles from the attribute extraction features [19] of
all the product images. Formally, let $\mathbf{A}\in\mathbb{R}^{M\times N}$
indicate the confidence that each of the M visual attributes is contained in
each of the N images. $\mathbf{A}$ can be factorized into two matrices with
non-negative entries:

$\mathbf{A}\approx\mathbf{W}\mathbf{H},\mathbf{W}\in\mathbb{R}^{M\times
K}\quad\textrm{and}\quad\mathbf{H}\in\mathbb{R}^{K\times N}$ (4)

where $\mathbf{W}$ represents the confidence that each attribute is part of a
style and $\mathbf{H}$ represents the confidence that each style is associated
to an image. The popularity signal $y$ for a style $k$ is built by considering
the interactions in the Amazon Reviews dataset [24] of all the items
$\\{\mathbf{z}\\}\in A$ at time $t$, weighted by their style membership
$H(k,z)$. For a detailed explanation, we refer to [1].

Table 4: Average results over all Fashion Forward [1] dataset partitions and
specific results for the Dresses partition, where POP outperforms even the
original GT style popularity time series (_Oracle_).

Global Average
---
Signals | Mean | Last | Drift | AR | ARIMA | SES
| MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE
_Oracle_ | _0.136_ | _0.170_ | _0.093_ | _0.114_ | _0.174_ | _0.222_ | _0.271_ | _0.403_ | _0.136_ | _0.167_ | _0.094_ | _0.116_
GoogleTrends | 0.846 | 1.000 | 0.846 | 1.000 | 0.846 | 1.000 | 0.846 | 1.000 | 0.846 | 1.000 | 0.846 | 1.000
POP | 0.152 | 0.192 | 0.116 | 0.144 | 0.182 | 0.229 | 0.281 | 0.418 | 0.235 | 0.293 | 0.125 | 0.156
Dresses
Signals | Mean | Last | Drift | AR | ARIMA | SES
| MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE
_Oracle_ | _0.155_ | _0.197_ | _0.130_ | _0.158_ | _0.203_ | _0.263_ | _0.307_ | _0.409_ | _0.173_ | _0.209_ | _0.129_ | _0.157_
GoogleTrends | 0.849 | 1.000 | 0.849 | 1.000 | 0.849 | 1.000 | 0.849 | 1.000 | 0.849 | 1.000 | 0.849 | 1.000
POP | 0.119 | 0.157 | 0.108 | 0.127 | 0.173 | 0.216 | 0.229 | 0.334 | 0.162 | 0.193 | 0.109 | 0.130

To extend this problem to a NFPPF setup, we have to imagine we are evaluating
the performance of a brand new style that does not have a past. The purpose of
POP becomes replacing the original style popularity series. This means that
POP has to be modified to deal with a style and not with a single clothing
item, where two challenges are presented: 1) To verify how similar POP is to
the ground truth popularity signal and; 2) To check if POP is highly
predictive of the future popularity.

To deal with the first challenge, we consider for each style $k$ the 2 textual
attributes [19] $w_{1},w_{2}$ (extracted from $\mathbf{W}$) with the highest
confidence scores and use them for the time dependent query expansion. FF
provides the only dataset for style forecasting where both images and product
metadata are available. The task is to predict a popularity score on a yearly
basis. The data ranges from $[2008-2013]$, but since Google Images returns
little to no images for queries before 2010, we use the range $[2010-2013]$ in
our experiments. We set $K_{past}=208$, meaning that we investigate 4 years
back. In this way we can create a weekly series for each year and use the
average as the value representing the popularity for that year. As probe image
to create our POP signal, we consider the top 10 images $\\{\mathbf{z}\\}$
that represent a style (based on their membership weight $H(k,z)$). Each image
will lead to one POP signal, which we average together to obtain the _POP
style signal_. This process is repeated for all the dataset partitions
presented in FF. To deal with the second challenge, we adopt the best
performing statistical forecasting techniques from Fashion Forward and feed
them the style POP signal described above. For more details on the forecasting
techniques, we refer the reader to [14, 4]:

  1. 1.

Naive methods. These methods infer by utilizing general information from the
training data. _Mean_ forecasts the future as the mean of past observations,
while _Last_ as the last observed value. _Drift_ is the same as _Last_ , but
the forecasts change over time based on the global trend of the series;

  2. 2.

Auto Regressive and Moving Average methods. These methods forecast using a
linear combination of _some_ past observations in a regression framework. The
most famous and representative method of this class is the _AutoRegressive
Integrated Moving Average_(ARIMA) model.

  3. 3.

Simple Exponential Smoothing. Stands for simple exponential smoothing, is a
weighted average of previous observations where the weights decrease
exponentially as we go further in the past.

Following the protocol of [1], all models are trained on all but the last
timestep, which is used for testing. We utilise the mean absolute percentage
error (MAPE) and the mean absolute error (MAE) to evaluate the forecasting
accuracy on the last timestep of the signal stemming from FF. To provide an
additional comparison, we show additional results using Google Trends as the
substitute popularity time series [35]. Note that to obtain fair and
comparable results, all the signals are rescaled in the range [0,1] using min-
max normalization. The results are shown in Table 4, where Oracle refers to
the original ground-truth style popularity series given as input to the
forecasting models.

Figure 7: Qualitative results on the forecasting of two different styles from
FF, represented by their respective ”style-defining” images and top
(automatically extracted) attributes. In both cases, POP and the ground-truth
(GT) style popularity from Fashion Forward are substantially similar. The plot
on the right shows a forecasting failure case, which holds for both POP and
the GT).

POP proves to be a natural substitute to the GT style popularity time series
and it allows for optimal forecasts, providing better results than the GT
signal itself for the _Dresses_ partition. On the other hand, Google Trends
are not able to convey such similarities, partially because searching only for
the popularity of textual tags might not provide a series that is as
predictive as ours.

##  5 Conclusion

Metaphorically, our approach performs a kind of “time travel”: it sends a
fashion probe image in the past, before its launch in the market. It then
models the popularity from that past point forward by relying on highly ranked
web images, queried by using general textual tags related to the probe. The
probe similarity with the past is then shown to be a good exogenous indicator
for future performance. This pipeline provides a new, effective and data-
centric scheme for NFPPF problems.

##### Acknowledgements.

This work was supported by the Italian MIUR through the project ”Dipartimenti
di Eccellenza 2018-2022”.

##  6 Appendix

The supplementary material discusses the following topics, in order:

  1. 1.

Additional ablation study on the signal forming step of POP (Sec. 6.1);

  2. 2.

Details on the training data and forecasting horizon on the VISUELLE dataset
(Sec. 6.2);

  3. 3.

Complete results on all Fashion Foward (FF) dataset partititions for the new
style popularity forecasting task (Sec. 6.3);

  4. 4.

Qualitative demonstrations of the downloaded images from our temporal cross-
modal query expansion, along with interesting insights discovered on the
fashionability of different categories and colors (Sec. 6.4);

  5. 5.

Inspired by latest, established guidelines in CV & ML research, a discussion
on ethical, social and economical implications of our work (Sec. 6.7).

###  6.1 Additional POP ablations

This section provides an additional ablation related to the ones shown in Sec.
4.1 and Tab. 3 in the main paper, concerning the signal forming step of the
POP signal. This is done by varying the embeddings of the cleaned images by CL
that are used as input to Equation 3 (Sec. 3.5 in the main paper), based on
the following strategies:

  * •

_Negative_ : it indicates the average distance of $\mathbf{z}$ with the
_pruned unfashionable images_
${}^{(-)}\\{\mathbf{x}^{\prime}_{i}\\}_{i=1,\ldots,M^{\prime\prime(t-k)}}^{(t-k)}$,
substituting the positive ones from Equation 3;

  * •

_Positive and Negative_ : here we fed into the forecasting approach two
signals, the original POP and the _Negative_ one, making POP a multivariate
(2D) time series.

We discover that the _Negative_ approach gives some boost, probably accounting
for how much the probe has to be dissimilar to unfashionable items. On the
other hand, _Positive and Negative_ shows a decrease in comparison to using
only the pruned fashionable images, probably because the two signals are
complementary (Tab. 5). Nevertheless, all the ablated approaches still provide
an exogenous time series that helps models perform better when they consider
it as additional input.

Table 5:  Alternative versions of the signal forming step, comparing to the
one proposed in Sec. 3.5, represented here as POP, on both the _first order
setup_ and _release setup_ of VISUELLE. Lower is better for both metrics.

| _First Order Setup_ | _Release Setup_
---|---|---
Strategy | WAPE | MAE | WAPE | MAE
_Negative_ | 52.68 | 28.78 | 53.90 | 29.44
_Positive and Negative_ | 52.97 | 28.94 | 54.35 | 29.69
POP | 52.39 | 28.62 | 53.41 | 29.18

###  6.2 Additional details on VISUELLE

The VISUELLE dataset from [35] contains the following, multi-modal information
for each product: i) Images, ii) Text tags, iii) Google Trends, iv) Sales
curves.

  * •

Images: RGB images with an average size of $577\times 227$;

  * •

Text: Three types of text tags (category, dominant color, fabric) that come
partially from the technical sheet of the product (category, fabric), while
the dominant color has been automatically extracted by the authors of the
dataset.

  * •

Google Trends: Three 52-step long time series for each product, one for each
of the tags listed above, extracted by the Google Trend platform in robust way
[25] for the 52 weeks prior the sale start date;

  * •

Sales curve: 12-step long weekly time series, reporting the sales of a
particular item.

The testing product set is composed of the 497 most novel products of the two
most recent seasons (Spring-Summer and Autumn-Winter 2019). The rest of the
dataset (5080 products) is used for training. We _utilise all of this data_
for the experiments in Sec 4.1 of the main paper. Additional forecasting
results are displayed in Fig. 8 (in terms of WAPE), exhibiting the behaviour
of the best performing model for all possible forecasting horizons.

Figure 8: WAPE for different forecasting horizons and exogenous signals, using
GTM-Transformer [35] on the VISUELLE dataset. After six weeks there is a long
enough history to model tendencies in the sales without considering product
discounts or replenishments, unlike longer horizons. This is also reflected in
the WAPE values, which keep increasing for forecasting horizons longer than
six weeks. POP improves the forecasts for any horizon.

###  6.3 Complete results on Fashion Forward

We show the full new style popularity forecasting results on all dataset
partitions of Fashion Forward in Tab. 6, providing an extended version of Tab.
4 from the main text. The results show how POP can be used to estimate the
intra dataset popularity trends of products from Fashion Forward with
relatively high accuracy for all partitions. We would like to emphasize again
that we use POP as input to forecasting models and compare the predictions
with the ground-truth testing set that comes from the FF time series.

Table 6: Results across all the Fashion Forward [1] datasets.

Global Average
---
Signals | Mean | Last | Drift | AR | ARIMA | SES
| MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE
_Oracle_ | _0.136_ | _0.170_ | _0.093_ | _0.114_ | _0.174_ | _0.222_ | _0.271_ | _0.403_ | _0.136_ | _0.167_ | _0.094_ | _0.116_
GoogleTrends | 0.846 | 1.000 | 0.846 | 1.000 | 0.846 | 1.000 | 0.846 | 1.000 | 0.846 | 1.000 | 0.846 | 1.000
POP | 0.152 | 0.192 | 0.116 | 0.144 | 0.182 | 0.229 | 0.281 | 0.418 | 0.235 | 0.293 | 0.125 | 0.156
Dresses
Signals | Mean | Last | Drift | AR | ARIMA | SES
| MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE
_Oracle_ | _0.155_ | _0.197_ | _0.130_ | _0.158_ | _0.203_ | _0.263_ | _0.307_ | _0.409_ | _0.173_ | _0.209_ | _0.129_ | _0.157_
GoogleTrends | 0.849 | 1.000 | 0.849 | 1.000 | 0.849 | 1.000 | 0.849 | 1.000 | 0.849 | 1.000 | 0.849 | 1.000
POP | 0.119 | 0.157 | 0.108 | 0.127 | 0.173 | 0.216 | 0.229 | 0.334 | 0.162 | 0.193 | 0.109 | 0.130
Shirts
Signals | Mean | Last | Drift | AR | ARIMA | SES
| MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE
_Oracle_ | _0.122_ | _0.149_ | _0.075_ | _0.097_ | _0.148_ | _0.190_ | _0.301_ | _0.371_ | _0.126_ | _0.159_ | _0.080_ | _0.103_
GoogleTrends | 0.840 | 1.000 | 0.840 | 1.000 | 0.840 | 1.000 | 0.840 | 1.000 | 0.840 | 1.000 | 0.840 | 1.000
POP | 0.144 | 0.175 | 0.109 | 0.152 | 0.166 | 0.215 | 0.274 | 0.336 | 0.139 | 0.189 | 0.111 | 0.151
Tops&Tees
Signals | Mean | Last | Drift | AR | ARIMA | SES
| MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE | MAE | MAPE
_Oracle_ | _0.132_ | _0.165_ | _0.074_ | _0.087_ | _0.172_ | _0.212_ | _0.206_ | _0.429_ | _0.108_ | _0.133_ | _0.073_ | _0.087_
GoogleTrends | 0.848 | 1.000 | 0.848 | 1.000 | 0.848 | 1.000 | 0.848 | 1.000 | 0.848 | 1.000 | 0.848 | 1.000
POP | 0.193 | 0.245 | 0.131 | 0.153 | 0.206 | 0.257 | 0.341 | 0.585 | 0.405 | 0.497 | 0.156 | 0.186

Figure 9:  Qualitative results on VISUELLE, considering all the 12 time-steps.
In all the cases, using POP gives better results than not using it or using
other, similar exagenous series.

###  6.4 Qualitative Results

In this section we report a qualitative analysis of the POP signal, which in
the main paper was limited to Fig.3 due to space limitations. These results
give additional insight on the significance of our time-dependent, data-
centric approach.

###  6.5 Under the hood of the POP signal

In Fig. 10 and Fig. 11 we report two examples of the (automatically)
downloaded images used for the formation of the POP signal. In both figures,
the probe images from which we extract the textual attributes to index the
search are depicted. The analysis for each figure is reported in the
corresponding caption. We also report in the figures some pruned images by the
confident learning step, marked by a red cross.

Figure 10: Examples of images downloaded for the query ‘Grey Long Sleeves”
(after pruning by confident learning). One may note that mismatching images
are very few, intended as those images which are not containing any ”Grey Long
Sleeves”. An example would be the green sleeve + blue jeans in the bottom row.
It is worth noting how most of the fashionable items have no printed logos,
texture or tight sleeves. On the contrary,“Unfashionable Grey Long Sleeves”
have big logo on them, with a winter theme, and many colors accompanying a
gray background. In some cases, the gray color actually covers a small portion
of the clothing item. Pruned images are marked with a red cross.

Figure 11: Examples of images downloaded for the query ‘Violet Long Sleeve”
(after pruning by confident learning). The “Fashionable Violet Long Sleeve”
items seem to have a darker tone in most cases. Very long sleeves fade into
dresses, indicating the length of the garment as an important aspect for
making it fashionable. Curiously, “Unfashionable Violet Long Sleeve” contain
brighter colors, short garments (like pyjamas) with writings or printed
images. Pruned images are marked with a red cross.

###  6.6 The importance of a time-dependent query

In the main paper, we report in Tab. 3 various ablation studies on POP. Here
we focus on two aspects in particular: the “Time Dependent Query Expansion”,
and the “misaligned Past”. The obtained results suggest that exploiting
(un)fashionable images not related to the date of delivery on the market gives
worse results in terms of forecasting. Fig. 12 qualitatively demonstrates why
this is the case. As it is visible, what made a garment of a particular type
and color fashionable in 2017 (Fig. 12, top) does not correspond to the same
visual elements that can be found in 2019 (Fig. 12, bottom). More
specifically, throughout the spring/summer season of 2017, the green kimonos
tend to be heavily associated with white patterns and the color white in
general. In 2019, the kimonos are almost all in different shades of green or
even dark green.

Figure 12: Examples of Fashionable downloaded images for particular time-
depended queries. In this particular case, for the query ”green kimono dress”,
it can be seen how the notion of fashionability can have significant
variations over time. Notably, green kimonos in 2017, as seen in the latter
half of the first figure, tend to be heavily associated with white patterns
and the color white in general. In 2019, this trend appears to be dying out,
with the kimonos being of different shades of green or even dark green.

###  6.7 Ethical Concerns and Societal Impact

Ethical implications could, in principle, arise from the web image search:
observed images can, for example, contain copyrighted images. Nevertheless,
just as a normal user would use Google Images to gather an opinion of what
could be trending in fashion, so do we, albeit automatically. In particular,
we do not need to personally look at the web images (apart for the ones shown
in the paper and here for explanatory purposes), since POP is a just a
numerical time series. As for the societal impact, our approach can be highly
beneficial for fast fashion, which is the third most polluting industry in the
world. Having a precise estimation of sales or popularity can improve the
situation by solving supply chain issues and our pipeline can play a leading
part. Ameliorated forecasts with POP also have a big impact at the economic
level, in terms of profit. The best forecasting model on the VISUELLE dataset
(which uses our generated time series, as seen in Tables 1 and 2 in the main
paper), allows to spare 21% w.r.t. ordinary guidelines for new fast-fashion
products, reducing a loss of $4.390.400 US dollars to $3.491.600 US dollars,
assuming a general price of 28$ per piece for all products (independently on
the category).

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