10,051 research outputs found
Effects of White Space on Consumer Perceptions of Value in E-Commerce
As e-commerce becomes an increasingly large industry, questions remain about how the isolated effects of design elements on websites influence consumer perceptions and purchasing behavior. This study used a quantitative approach to measuring the effect of a ubiquitous element of design, white space, on the perception of the monetary value of individual items. White space is a key component of design and website usability, yet it has been shown to be related to the perception of luxury. Little is known about the direct relationship between manipulation of white space and the outcomes on consumer perceptions of value in an e-commerce context. This study found no significant difference between two levels of total white space area (large vs. small) measured by participants\u27 perceived cost of items (chairs). In contrast, while holding total white space constant, the effect of white space distance between images was significant for males but not for females. Additionally, no significant relationship between gender and frequency of online shopping behavior was found, χ2(1) = 3.19, p = .07, ϕ = .17. Gender and amount of time spent per month online were significantly related, χ2(1) = 6.21, p = .013, ϕ = .24
A Reminiscence of ”Mastermind”: Iris/Periocular Biometrics by ”In-Set” CNN Iterative Analysis
Convolutional neural networks (CNNs) have
emerged as the most popular classification models in biometrics
research. Under the discriminative paradigm of pattern
recognition, CNNs are used typically in one of two ways: 1)
verification mode (”are samples from the same person?”), where
pairs of images are provided to the network to distinguish
between genuine and impostor instances; and 2) identification
mode (”whom is this sample from?”), where appropriate feature
representations that map images to identities are found. This
paper postulates a novel mode for using CNNs in biometric
identification, by learning models that answer to the question ”is
the query’s identity among this set?”. The insight is a reminiscence
of the classical Mastermind game: by iteratively analysing the
network responses when multiple random samples of k gallery
elements are compared to the query, we obtain weakly correlated
matching scores that - altogether - provide solid cues to infer
the most likely identity. In this setting, identification is regarded
as a variable selection and regularization problem, with sparse
linear regression techniques being used to infer the matching
probability with respect to each gallery identity. As main strength,
this strategy is highly robust to outlier matching scores, which
are known to be a primary error source in biometric recognition.
Our experiments were carried out in full versions of two
well known irises near-infrared (CASIA-IrisV4-Thousand) and
periocular visible wavelength (UBIRIS.v2) datasets, and confirm
that recognition performance can be solidly boosted-up by the
proposed algorithm, when compared to the traditional working
modes of CNNs in biometrics.info:eu-repo/semantics/publishedVersio
LatentSwap3D: Semantic Edits on 3D Image GANs
3D GANs have the ability to generate latent codes for entire 3D volumes
rather than only 2D images. These models offer desirable features like
high-quality geometry and multi-view consistency, but, unlike their 2D
counterparts, complex semantic image editing tasks for 3D GANs have only been
partially explored. To address this problem, we propose LatentSwap3D, a
semantic edit approach based on latent space discovery that can be used with
any off-the-shelf 3D or 2D GAN model and on any dataset. LatentSwap3D relies on
identifying the latent code dimensions corresponding to specific attributes by
feature ranking using a random forest classifier. It then performs the edit by
swapping the selected dimensions of the image being edited with the ones from
an automatically selected reference image. Compared to other latent space
control-based edit methods, which were mainly designed for 2D GANs, our method
on 3D GANs provides remarkably consistent semantic edits in a disentangled
manner and outperforms others both qualitatively and quantitatively. We show
results on seven 3D GANs (pi-GAN, GIRAFFE, StyleSDF, MVCGAN, EG3D, StyleNeRF,
and VolumeGAN) and on five datasets (FFHQ, AFHQ, Cats, MetFaces, and CompCars).Comment: The paper has been accepted by ICCV'23 AI3DC
Coding Strategies Underlying Visual Processing
Acquiring and representing knowledge about our environment involves a variety of core neural computations. The coding strategies underlying visual perception highlight many of these processes, and thus reveal general design principles in perception and cognition. I will review three studies where I have used different computational frameworks and analyses to address open questions in visual coding. The first project uses factor analyses of individual differences in perception to demonstrate fundamentally different representational structures for the stimulus features of color and motion. In the second project, I have explored visual adaptation in the context of population coding to address controversies regarding which coding schemes are implicated by different patterns of adaptation aftereffects. In the third, I have explored these adaptation effects in the context of Bayesian inference. This approach accounts for the full gamut of known aftereffects within the context of physiologically plausible models and provides principled quantitative predictions for why and how much the system should adapt. Together, these projects draw on the power of formal computational approaches both for analyzing neural representations and for revealing the computations and coding principles on which they are based
Beyond Identity: What Information Is Stored in Biometric Face Templates?
Deeply-learned face representations enable the success of current face
recognition systems. Despite the ability of these representations to encode the
identity of an individual, recent works have shown that more information is
stored within, such as demographics, image characteristics, and social traits.
This threatens the user's privacy, since for many applications these templates
are expected to be solely used for recognition purposes. Knowing the encoded
information in face templates helps to develop bias-mitigating and
privacy-preserving face recognition technologies. This work aims to support the
development of these two branches by analysing face templates regarding 113
attributes. Experiments were conducted on two publicly available face
embeddings. For evaluating the predictability of the attributes, we trained a
massive attribute classifier that is additionally able to accurately state its
prediction confidence. This allows us to make more sophisticated statements
about the attribute predictability. The results demonstrate that up to 74
attributes can be accurately predicted from face templates. Especially
non-permanent attributes, such as age, hairstyles, haircolors, beards, and
various accessories, found to be easily-predictable. Since face recognition
systems aim to be robust against these variations, future research might build
on this work to develop more understandable privacy preserving solutions and
build robust and fair face templates.Comment: To appear in IJCB 202
Gibbs sampling with people
A core problem in cognitive science and machine learning is to understand how
humans derive semantic representations from perceptual objects, such as color
from an apple, pleasantness from a musical chord, or seriousness from a face.
Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying
such representations, in which participants are presented with binary choice
trials constructed such that the decisions follow a Markov Chain Monte Carlo
acceptance rule. However, while MCMCP has strong asymptotic properties, its
binary choice paradigm generates relatively little information per trial, and
its local proposal function makes it slow to explore the parameter space and
find the modes of the distribution. Here we therefore generalize MCMCP to a
continuous-sampling paradigm, where in each iteration the participant uses a
slider to continuously manipulate a single stimulus dimension to optimize a
given criterion such as 'pleasantness'. We formulate both methods from a
utility-theory perspective, and show that the new method can be interpreted as
'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation
parameter to the transition step, and show that this parameter can be
manipulated to flexibly shift between Gibbs sampling and deterministic
optimization. In an initial study, we show GSP clearly outperforming MCMCP; we
then show that GSP provides novel and interpretable results in three other
domains, namely musical chords, vocal emotions, and faces. We validate these
results through large-scale perceptual rating experiments. The final
experiments use GSP to navigate the latent space of a state-of-the-art image
synthesis network (StyleGAN), a promising approach for applying GSP to
high-dimensional perceptual spaces. We conclude by discussing future cognitive
applications and ethical implications
Gibbs sampling with people
A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
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