-9FAT4oBgHgl3EQfqR39/content/tmp_files/2301.08647v1.pdf.txt

Image Memorability Prediction with Vision
Transformers
Thomas Hagen1,� and Thomas Espeseth1,2
1Department of Psychology, University of Oslo, Oslo, Norway
2Department of Psychology, Oslo New University College, Oslo, Norway
Behavioral studies have shown that the memorability of images
is similar across groups of people, suggesting that memorability
is a function of the intrinsic properties of images, and is unre-
lated to people’s individual experiences and traits. Deep learn-
ing networks can be trained on such properties and be used
to predict memorability in new data sets. Convolutional neu-
ral networks (CNN) have pioneered image memorability predic-
tion, but more recently developed vision transformer (ViT) mod-
els may have the potential to yield even better predictions. In
this paper, we present the ViTMem, a new memorability model
based on ViT, and evaluate memorability predictions obtained
by it with state-of-the-art CNN-derived models. Results showed
that ViTMem performed equal to or better than state-of-the-
art models on all data sets. Additional semantic level analyses
revealed that ViTMem is particularly sensitive to the seman-
tic content that drives memorability in images. We conclude
that ViTMem provides a new step forward, and propose that
ViT-derived models can replace CNNs for computational pre-
diction of image memorability. Researchers, educators, adver-
tisers, visual designers and other interested parties can leverage
the model to improve the memorability of their image material.
memorability | vision transformers | psychology | semantic information
Introduction
Everyone knows that our memories depend on the experi-
ences we have had, facts we have encountered, and the abil-
ities we have to remember them. Combinations of these fac-
tors differ between individuals and give rise to unique memo-
ries in each of us. However, a complementary perspective on
memory focuses on the material that is (to be) remembered
rather than the individual that does the remembering. In one
central study, Isola et al. (1) presented more than 2000 scene
images in a continuous repeat-detection task. The partici-
pants were asked to respond whenever they saw an identical
repeat. The results revealed that the memorability score (per-
cent correct detections) varied considerably between images.
Most importantly, by running a consistency analysis in which
Spearman’s rank correlation was calculated on the memo-
rability scores from random splits of the participant group,
Isola and colleagues (1) were able to show that the memora-
bility score ranking was consistent across participants – some
images were memorable and some were forgettable. These
results indicate that the degree to which an image was cor-
rectly detected depended on properties intrinsic to the image
itself, not the traits of the observers. This is important be-
cause it shows that one can use the memorability scores in a
stimulus set to predict memory performance in a new group
of participants.
These results have been replicated and extended in a num-
ber of studies, revealing that similar findings are obtained
with different memory tasks (2), different retention times
(1, 2), different contexts (3), and independent of whether en-
coding is intentional or incidental (4). However, although
image memorability has proven to be a robust and reliable
phenomenon, it has not been straightforward to pinpoint the
image properties that drive it. What seems clear though, is
that memorability is multifaceted (5, 6). One way to char-
acterize the underpinnings of memorability is to investigate
the contribution from processes at different levels of the vi-
sual processing stream. For example, at the earliest stages of
processing of a visual scene, visual attributes such as local
contrast, orientation, and color are coded. At an intermedi-
ate level, contours are integrated, surfaces, shapes, and depth
cues are segmented, and foreground and background are dis-
tinguished. At a higher level, object recognition is conducted
through matching with templates stored in long term mem-
ory.
Positive correlations between brightness and high contrast of
objects with memorability has been found (7), but in general,
low-level visual factors such as color, contrast, and spatial
frequency do not predict memorability well (5, 8, 9). This
is consistent with results showing that perceptual features
are typically not retained in long term visual memory (10).
In contrast to the low-level features, the evidence for a re-
lation between intermediate to high level semantic features
and memorability is much stronger. For example, images that
contain people, faces, body parts, animals, and food are often
associated with high memorability, whereas the opposite is
a typical finding for objects like buildings and furniture and
images of landscapes and parks (3, 7, 11, 12). Other inter-
mediate to high level features such as object interaction with
the context or other objects, saliency factors, and image com-
position also contribute to memorability (5). Furthermore,
although memorability is not reducible to high-level features
such as aesthetics (1, 12), interestingness (1, 13), or popu-
larity (12), emotions, particularly of negative valence, seem
to predict higher memorability (9, 12). Finally, memorabil-
ity seems to be relatively independent of cognitive control,
attention, or priming (14).
Overall, the available evidence indicates that memorability
seems to capture intermediate- to high-level properties of
semantics, such as objects or actions, and image composi-
tion, such as layout and clutter, rather than low-level fea-
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tures (5, 15). This fits well with the central role of semantic
categories in organizing cognition and memory (16). Gen-
erally, the priority of semantic-level information enables us
to quickly understand novel scenes and predict future events
(17). For example, when inspecting a novel scene or an im-
age, we do not primarily focus on low-level perceptual fea-
tures or pixels, but prioritize more abstract visual schemas
involving spatial regions, objects, and the relation between
them (18). Also, when people are asked to indicate which
regions of an image helps them recognize an image, there is
high consistency between people’s responses (18). Similarly,
fixation map data from eye-tracking have shown that there is
a positive correlation between fixation map consistency and
scene memorability, and this relation is associated with the
presence of meaningful objects (3, 7, 19). Bylinskii et al.
(5) suggest that these properties most efficiently signal infor-
mation of high utility to our species, for example, emotions,
social aspects, animate objects (e.g., faces, gestures, interac-
tions), unexpected events, and tangible objects.
Memorability prediction. The finding that the memorabil-
ity of an image is governed by properties intrinsic to the im-
age itself, not only implies that one can predict memory per-
formance in a new set of participants, as described above,
but also that one can predict the memorability of a novel set
of images (i.e., memorability is an “image computable” fea-
ture). Given the availability of computational algorithms and
high-quality training sets of sufficient size, one can predict
memorability in novel sets of images for future (or already
conducted) behavioral or neuroimaging studies. Such mem-
orability prediction could also be valuable in a number of ap-
plied settings (e.g., within education, marketing and human-
computer interaction).
Memorability researchers have employed computer vision
models such as convolutional neural networks (CNNs) from
early on (12), and advancements in the field have allowed
researchers to predict image memorability with increasing
precision (20–22). The inductive bias (the assumptions of
the learning algorithms used to generalize to unseen data) of
CNNs is inspired by knowledge about the primate visual sys-
tem, and activations in the networks layers have, with some
success, been used to explain neural activations (23). How-
ever, some vulnerabilities of CNNs have been noted. For ex-
ample, CNNs appear to depend more on image texture than
biological vision systems do (24), and have problems with
recognizing images based on the shape of objects (e.g., when
texture is suppressed or removed). However, this vulnera-
bility is reduced when the model’s shape bias is increased
through training on shape representations (25).
The LaMem train/test splits is a well-established benchmark
for memorability prediction (12). The original MemNet (12),
which is based on AlexNet (26), achieved a Spearman rank
correlation of 0.64 on this benchmark.
There have been
several improvements on this benchmark, the leading ap-
proaches utilize image captioning to enhance memorability
predictions. That is, a CNN produces a textual description of
the image, which is then used to provide more high-level se-
mantic information which is embedded into a semantic vec-
tor space before being combined with CNN image features
in a multi-layered perceptron network. Squalli-Houssaini et
al. (21) used this approach to reach a Spearman correlation
of 0.72, with a mean squared error (MSE) of approximately
0.0092 (22). Leonardi et al. (22) used the captioning ap-
proach with dual ResNet50s and a soft attention mechanism
to reach a rank correlation of 0.687 with an MSE of 0.0079.
The ResMem model (20), which is a CNN-based residual
neural network architecture (ResNet), uses LaMem, but also
takes advantage of a more recently published dataset named
MemCat (11). This is a data set containing 10,000 images
based on categories of animals, food, landscape, sports and
vehicles. This data set also has a higher split half correla-
tion than LaMem. Needell and Bainbridge (20) argue that
the LaMem dataset on its own is lacking in generalizability
due to poor sampling of naturalistic images. That is, the im-
ages are more intended as artistic renderings designed to at-
tract an online audience. Hence by combining MemCat with
LaMem this should potentially yield a more generalizable
model. Moreover, the increased size of the combined dataset
might help in driving the model performance further than pre-
vious models based on LaMem. The authors of ResMem
also noted the importance of semantic information and struc-
tured their approach to utilize semantic representations from
a ResNet model in order to improve predictions. An added
benefit of ResMem is that it is shared on the python pack-
age index, which makes it easily accessible to researchers in
diverse fields.
Vision transformers. Vision transformers (ViT) have re-
cently been shown to provide similar or better performance
than CNNs in a variety of computer vision tasks (27). This
architecture was first introduced in the natural language pro-
cessing field (28) for capturing long-range dependencies in
text. This architecture leads to superior speed/performance
balance relativ to ResNet architectures (29). Moreover, ViTs
have been shown to produce errors that are more similar to
human errors (30), suggesting that they could take similar
information into account (see also (31)). A reason for this
may be that ViTs are likely to take more of the global context
into account and be more dependent on the shape of objects
rather than their texture (30). While it is not entirely clear
why such properties may yield better predictions of image
memorability, it could still help inform the discourse on the
visual characteristics that are relevant as well as potentially
yielding a better model for predicting image memorability.
Hence, we set out to investigate if vision transformers can
yield better predictions of memorability than the state-of-
the-art in image memorability prediction. In particular, we
aimed to (i) benchmark a model based on ViT against the
well-established LaMem train/test splits (12), (ii) train a ViT
against the combined LaMem and MemCat data sets (20) to
benchmark against the ResMem model (20), (iii) train a final
ViT model against a more diverse and deduplicated data set,
(iv) validate the final ViT model against additional indepen-
dent data sets and (v) inspect semantic level distributions of
memorability scores for behavioral and predicted data.
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Methods
As our model is based on ViT to predict memorability we
named it ViTMem.
Because it has been shown that low-
level visual features are less important for image memorabil-
ity prediction, it would seem appropriate to use image aug-
mentations in training our ViTMem model to reduce over-
fitting. This approach have also been used by others (22),
although not to the extent done here.
The augmentations
used consisted of horizontal flipping, sharpen, blur, motion
blur, random contrast, hue saturation value, CLAHE, shift
scale rotate, perspective, optical distortion and grid distortion
(32). For training all models we used PyTorch, the ADAM
optimizer and mean squared error (squared L2 norm) for the
loss function. Images were input as batches of 32 in RGB
and resized to 256x256 pixels before applying augmentations
with a probability of 0.7 and center cropping to 224x224 pix-
els. For creating ViTMem we used transfer learning on a
vision transformer (27) model pretrained on ImageNet 1k
(vit_base_patch16_224_miil) (33).
The final classification
layer was reduced to a single output and a sigmoid activation
function.
As we aim to provide an accessible model to the re-
search community, it is also necessary to compare against
the publicly available ResMem model. Unfortunately, the
authors of ResMem did not publish their held-out test
set, hence it is difficult to make a balanced compari-
son between the currently published ResMem model and
any competing models.
We propose to do 10 train/test
splits that can be used by future researchers (available
at https://github.com/brainpriority/vitmem_data). Moreover,
ResMem was not benchmarked on LaMem, hence a fair com-
parison can only be made on the combined LaMem and
MemCat data set.
For the semantic level analysis, we chose to use image cap-
tioning (34) as this provides an efficient method for deriv-
ing semantic properties from images at scale. Importantly,
as the image captioning model was trained on human image
descriptions, it is likely to extract content that humans find
meaningful in images, and in particular objects and contexts
that are relevant for conveying such meanings. Hence, nouns
derived from such descriptions are likely to be representative
portions of the content that would convey meaning to humans
observing the images.
Data Sources. For the large-scale image memorability
(LaMem) benchmark we used the LaMem dataset (12). The
image set used by ResMem is a combination of the image sets
LaMem (12) and MemCat (11). LaMem containing 58,741
and MemCat 10,000 images, for a total of 68,741 images.
ResMem is reported to have used a held-out test set with 5000
images, hence we randomly selected 5000 images as our test
set for our 10 train/test splits for this combined data set. For
our final model we aimed to clean up the data and combine
more of the available data sets on image memorability. As
number of duplicated images within and between data sets is
unknown and duplicated images may interfere with perfor-
mance measures, we aimed to deduplicate the data for this
model. Duplicated images were identified by simply deriv-
ing embeddings from an off-the-shelf CNN model, and then
visually inspecting the most similar embeddings. Our analy-
sis of the data sets LaMem and MemCat showed that LaMem
have 229 duplicated images while MemCat have 4. More-
over, 295 of the images in LaMem is also in MemCat. We
aimed to build a larger and more diverse data set by com-
bining more sources, and for this we chose CVPR2011 (9)
and FIGRIM (3). CVPR2011 had 6 internal duplicates, 651
duplicates against LaMem, 78 against MemCat og 9 against
FIGRIM. FIGRIM had 20 duplicates against MemCat and 70
against LaMem. All identified duplicates were removed be-
fore merging the data sets. As the images from FIGRIM and
CVPR2011 were cropped, we obtained the original images
before including them in the data set. This resulted in a data
set with 71,658 images. For this data set we performed a 10%
split for the test set.
Results
Results on LaMem data set. On the LaMem data set
the ViTMem model reached an average Spearman rank
correlation of 0.711 and an MSE of 0.0076 (see Table 1).
Here we compare our performance to measures obtained by
MemNet (12), Squalli-Houssaini et al. (21) and Leonardi et
al. (22).
Table 1. Comparison of model performance on LaMem data set
Model
MSE Loss ↓
Spearman ρ ↑
MemNet
Unknown
0.640
Squalli-Houssaini et al.
0.0092
0.720
Leonardi et al.
0.0079
0.687
ViTMem
0.0076
0.711
Results on the combined LaMem and MemCat data
set. Training on 10 train/test splits on the combined data
set the results showed that ViTMem performed better than
the ResMem model (see Table 2). The average across splits
showed a Spearman rank correlation of 0.77 and an MSE of
0.005.
Table 2. Model performance on LaMem and MemCat combiend dataset
Model
MSE Loss ↓
Spearman ρ ↑
ResMem
0.009
0.67
ViTMem
0.005
0.77
Results on combined and cleaned data set. To assess
model performance on the larger and cleaned data set, we
made a train/test split and then performed repeated k-fold
cross validation with 10 train/test splits on the training set.
This resulted in a mean MSE loss of 0.006 and a mean
Spearman rank correlation of 0.76 (see Table 3). In order
to provide a model for the community we used the full data
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set to train the final model (ViTMem Final Model), which
is published on the python package index as version 1.0.0.
This was trained on the full training set and tested on its
corresponding test set. The results showed a Spearman rank
correlation of 0.77 and an MSE of 0.006 (see Table 3). The
train/test splits are available on github.
Table 3. Model performance on combined and cleaned data set
Model
MSE Loss ↓
Spearman ρ ↑
ViTMem
0.006
0.76
ViTMem Final Model
0.006
0.77
Validation on independent data sets. To further validate
our model, we used memorability scores from an indepen-
dent data set by Dubey and colleagues named PASCAL-S
(7, 35) consisting of 852 images and cropped objects from
the same images. ViTMem achieved a Spearman correlation
of 0.44 on the images and 0.21 on the objects. In compar-
ison ResMem achieved a correlation of 0.36 on the images
and 0.14 on the objects. Validating against the THINGS data
set (15), which consists of 26,106 images with memorabil-
ity scores, achieved a Spearman rank correlation of 0.30 for
ViTMem and 0.22 for ResMem.
Semantic level analysis. In order to better understand how
the model predictions relate to the semantic content of the
images, we performed image captioning (34) on the com-
bined LaMem and MemCat data set and the Places205 data
set (36). We extracted nouns from the resulting image de-
scriptions and averaged behavioral or predicted memorability
scores for each noun (37). That is, the memorability for each
image was assigned to each noun derived from the image cap-
tioning procedure. For the combined LaMem and MemCat
data set we averaged behavioral memorability scores over
nouns (see Figure 1), while for the Places205 data set we
averaged predicted memorability scores from the ViTMem
model (see Figure 2). A general interpretation of the visu-
alizations in Figure 1 and 2 is that they appear to reveal a
dimension from nouns usually observed outdoors to more in-
door related nouns and ending with nouns related to animals,
and in particular, humans. This would appear to reflect the
distributions observed in previous work (9, 15), and hence
help to validate the model in terms of the image content it
is sensitive to. To further investigate how well memorability
associated with nouns were similar across the models we se-
lected nouns occurring more than the 85th percentile in each
set (654 nouns for LaMem and MemCat, 2179 nouns for
Places205), this resulted in 633 matched nouns across sets.
Analysis of these showed a Spearman ranked correlation of
0.89 and a R2 of 0.79, p<0.001 (see Figure 3). This analysis
indicates that nouns from image captioning is a strong pre-
dictor of image memorability and that the ViTMem model is
able to generalize the importance of such aspects from the
training set to a new set of images.
Discussion
Using vision transformers we have improved on the state-
of-the-art in image memorability prediction. Results showed
that ViTMem performed equal to or better than state-of-
the-art models on LaMem, and better than ResMem on the
LaMem and MemCat hybrid data set. In addition, we assem-
bled a new deduplicated hybrid data set and benchmarked
the ViTMem model against this before training a final model.
The model was further validated on additional data sets, and
performed better than ResMem on these as well. Finally,
we ran a semantic level analysis by using image captioning
on the hybrid data set.
We ranked the behavioral memo-
rability scores on the images, labeled with nouns extracted
from the captioning procedure. The results revealed that im-
ages labeled by nouns related to landscapes, cities, buildings
and similar, were ranked lowest, whereas images labeled by
nouns related to animate objects and food, were ranked high-
est. This finding is consistent with known category effects
on memorability (3, 7, 11, 12, 15) and suggests that the la-
bels extracted from captioning procedure is strongly related
to factors that drive memorability for those images. Subse-
quently, we predicted memorability scores on images from a
novel data set (Places205), ran the image captioning proce-
dure, and ranked the predicted memorability scores on the
images, labeled with nouns extracted from the captioning
procedure. Visual inspection of the results revealed that the
ranks were similar across samples and methods. This impres-
sion was confirmed by a strong correlation between matching
pairs of nouns and 79% explained variance, suggesting that
ViTMem captures the semantic content that drives memora-
bility in images.
The use of image augmentations in training the ViTMem
model in combination with state-of-the-art performance sug-
gest that such augmentations are not disrupting the ability
of the model to predict image memorability and hence may
further support the importance of semantic level properties
in image memorability. That is, the augmentations modify
a range of low-level image properties but mostly leave the
semantic content intact.
In comparison with ResMem, which relies on a CNN-based
residual neural network architecture, ViTMem is based on
vision transformers which integrate information in a more
global manner (30). As images are compositions of several
semantically identifiable objects or parts of objects, a more
holistic approach may be more apt at delineating the relative
relevance of objects given their context. That is, we speculate
that a broader integration of image features allows for a more
complete evaluation of its constituent features in relation to
each other. Hence, if semantic content is important for pre-
dicting image memorability, the model may have weighed the
importance of semantic components in relation to each other
to a larger degree than models based on CNNs.
ViTMem code and train/test sets are shared on github
(https://github.com/brainpriority/), and a python package
named vitmem is available on the python package index (see
supplementary Sup. Note 1 for a tutorial). Researchers and
interested parties can use the model to predict memorability
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Memorability
c()
0.56
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0.72
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mountains
skyline
clouds
sunset
fireplace
view
cloud
dresser
pine
bedroom
houses
stream
church
highway
waterfall
house
hotel
sky
boat
wave
water
park
reflection
building
walls
tree
people
lights
temple
smoke
flowers
power
rock
group
bus
store
lot
truck
fire
center
market
game
walking
bench
court
person
guitar
police
motorcycle
food
men
show
picture
stem
sign
ground
link
women
stuffed
toy
phone
bride
plate
bag
girl
cards
wedding
shoe
pair
scarf
hands
hand
shape
neck
face
cut
toothbrush
half
banana
smile
makeup
necklace
teeth
pepper
valley
mountain
dining
lake
trees
buildings
river
hill
city
boats
rocks
fog
lobby
ocean
tables
middle
kitchen
area
bridge
construction
field
office
room
woods
road
clock
photo
steel
street
stove
surfboard
light
dirt
window
side
fence
train
bed
museum
door
bird
mirror
flower
grass
course
blue
video
row
car
couple
table
top
line
man
bug
case
dog
floor
gas
boy
girls
cell
piece
camera
woman
knife
arms
baby
board
gold
head
hair
sunglasses
persons
shirt
tie
feet
nose
chocolate
word
beard
snake
tattoo
blood
bikini
Fig. 1. Average behavioral image memorability scores for nouns that were extracted from images in the LaMem and MemCat data sets. The nouns shown are those that
occurred most frequently or that are more frequent in the English language (38).
Memorability
c()
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badlands
rim
stormy
glacier
mountain
sun
town
hill
fireplace
houses
sunset
snow
slope
desert
dusk
rocks
couches
city
cabinets
steeple
university
street
place
hotel
highway
cathedral
formation
center
building
beach
tree
home
way
stone
lot
fire
christmas
lighthouse
space
monument
desk
people
crowd
boat
wall
inside
airport
music
van
museum
model
round
stage
statue
baseball
auditorium
party
classroom
tent
stand
row
court
store
picture
pink
bus
shelf
bowling
sale
men
bars
family
gym
fish
boy
motel
woman
ring
rack
soldier
girl
ties
dresses
words
name
dancing
suit
wrestlers
arms
mannequin
cookies
cream
shirt
wife
cupcakes
chocolate
bikini
hillside
clouds
mountains
valley
cloud
farm
village
snowy
square
waves
vineyard
island
view
mansion
smoke
castle
living
coast
lawn
area
church
house
tower
clock
field
road
rain
wave
sink
top
state
water
chairs
bed
room
side
birds
dock
leaves
park
supplies
force
station
table
play
post
cross
market
desks
photos
group
image
library
game
line
school
video
dog
food
star
crib
show
clothes
book
floor
children
man
heart
baby
display
sign
roller
women
class
football
girls
case
hands
team
desserts
face
shirts
suits
logo
hair
plate
pastries
head
grave
meat
tie
bread
donuts
mouth
dance
dress
dancer
Fig. 2. Average ViTMem predicted image memorability scores for nouns that were extracted from images in the Places205 data set. The nouns shown are those that occurred
most frequently or that are more frequent in the English language (38).
0.6
0.7
0.8
0.9
0.6
0.7
0.8
0.9
Memorability for LaMem & MemCat Nouns (Behavioral)
Memorability for Places205 Nouns (ViTMem)
Fig. 3. Average memorability scores for images with matching nouns in different
data sets. The y-axis shows average predicted memorability scores from ViTMem
on the Places205 data set.
The x-axis shows average behavioral memorability
scores on the combined LaMem and MemCat data set.
in existing or novel stimuli and employ them in research or
applied settings. The ViTMem model will allow researchers
to more precisely predict image memorability. The release
of ViTMem follows up ResMem in providing an accessible
method for predicting image memorability. This is impor-
tant for studies aiming to control for how easily an image can
be remembered. This will for example allow experimental
psychologists and neuroscientists to better control their re-
search. Similarly, educators, advertisers and visual designers
can leverage the model to improve the memorability of their
content.
Despite state-of-the-art performance in memorability predic-
tion, improvements may still be possible to achieve. Previous
works have shown benefits of pretraining their networks on
data sets of places and objects prior to fine tuning for memo-
rability prediction (39). Moreover, ViTMem do not take im-
age captioning into account, which have been successfully
done with CNNs (21, 22). Hence there is potentially more
to be gained from incorporating image semantics and/or pre-
training on data sets of objects and places. In addition, ViT-
Mem is only based on the "base" configuration of the avail-
able ViT models. Model performance may still increase by
adopting the “large” or “huge” configurations of the model.
We conclude that ViTMem can be used to predict memora-
bility for images at a level that is equal to or better than state-
of-the-art models, and we propose that vision transformers
provide a new step forward in the computational prediction
of image memorability.
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ViTMem

Supplementary Note 1: How to use the vitmem python package
Python needs to be installed on a computer before pip can be used to install the vitmem package.
To install vitmem from a command prompt run:
pip install vitmem
To predict image memorability for an image named "image.jpg", run the following in a python interpreter:
from vitmem import ViTMem
model = ViTMem()
memorability = model("image.jpg")
print(f"Predicted memorability: {memorability}")
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