828 research outputs found
Gender Difference in Situation Awareness when Receiving Wayfinding Direction by Landmarks and Headings
In aviation, situation awareness (SA) is a fundamental requirement for effective flying and air traffic control. This skill has greatly been associated with pilot and air traffic controller performance. Previous studies in aviation and other fields have shown that gender differences exist in SA performance. Four hypotheses were tested in this study: women navigate better from landmark cues; men navigate better from headings cues; women have better SA performance than men when receiving landmark directions; and men have better SA when receiving cardinal directions. Thirty-eight participants drove a driving simulator twice. While driving, participants were asked SA questions to assess their SA performances. The results showed participants navigate better from landmark cues regardless of gender. Men showed poorer SA in landmark conditions than in headings conditions, but there was no significant difference in women. However, overall, women performed worse in response time to answering SA questions. This study can be beneficial for pilots’ selection tests and providing special training for male and female pilots
DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning
Large-scale text-to-image generation models with an exponential evolution can
currently synthesize high-resolution, feature-rich, high-quality images based
on text guidance. However, they are often overwhelmed by words of new concepts,
styles, or object entities that always emerge. Although there are some recent
attempts to use fine-tuning or prompt-tuning methods to teach the model a new
concept as a new pseudo-word from a given reference image set, these methods
are not only still difficult to synthesize diverse and high-quality images
without distortion and artifacts, but also suffer from low controllability.
To address these problems, we propose a DreamArtist method that employs a
learning strategy of contrastive prompt-tuning, which introduces both positive
and negative embeddings as pseudo-words and trains them jointly. The positive
embedding aggressively learns characteristics in the reference image to drive
the model diversified generation, while the negative embedding introspects in a
self-supervised manner to rectify the mistakes and inadequacies from positive
embedding in reverse. It learns not only what is correct but also what should
be avoided. Extensive experiments on image quality and diversity analysis,
controllability analysis, model learning analysis and task expansion have
demonstrated that our model learns not only concept but also form, content and
context. Pseudo-words of DreamArtist have similar properties as true words to
generate high-quality images
Adversarially-Aware Robust Object Detector
Object detection, as a fundamental computer vision task, has achieved a
remarkable progress with the emergence of deep neural networks. Nevertheless,
few works explore the adversarial robustness of object detectors to resist
adversarial attacks for practical applications in various real-world scenarios.
Detectors have been greatly challenged by unnoticeable perturbation, with sharp
performance drop on clean images and extremely poor performance on adversarial
images. In this work, we empirically explore the model training for adversarial
robustness in object detection, which greatly attributes to the conflict
between learning clean images and adversarial images. To mitigate this issue,
we propose a Robust Detector (RobustDet) based on adversarially-aware
convolution to disentangle gradients for model learning on clean and
adversarial images. RobustDet also employs the Adversarial Image Discriminator
(AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable
robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that
our model effectively disentangles gradients and significantly enhances the
detection robustness with maintaining the detection ability on clean images.Comment: ECCV2022 oral pape
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders
While sequential recommender systems achieve significant improvements on
capturing user dynamics, we argue that sequential recommenders are vulnerable
against substitution-based profile pollution attacks. To demonstrate our
hypothesis, we propose a substitution-based adversarial attack algorithm, which
modifies the input sequence by selecting certain vulnerable elements and
substituting them with adversarial items. In both untargeted and targeted
attack scenarios, we observe significant performance deterioration using the
proposed profile pollution algorithm. Motivated by such observations, we design
an efficient adversarial defense method called Dirichlet neighborhood sampling.
Specifically, we sample item embeddings from a convex hull constructed by
multi-hop neighbors to replace the original items in input sequences. During
sampling, a Dirichlet distribution is used to approximate the probability
distribution in the neighborhood such that the recommender learns to combat
local perturbations. Additionally, we design an adversarial training method
tailored for sequential recommender systems. In particular, we represent
selected items with one-hot encodings and perform gradient ascent on the
encodings to search for the worst case linear combination of item embeddings in
training. As such, the embedding function learns robust item representations
and the trained recommender is resistant to test-time adversarial examples.
Extensive experiments show the effectiveness of both our attack and defense
methods, which consistently outperform baselines by a significant margin across
model architectures and datasets.Comment: Accepted to RecSys 202
Domain Adaptation for Question Answering via Question Classification
Question answering (QA) has demonstrated impressive progress in answering
questions from customized domains. Nevertheless, domain adaptation remains one
of the most elusive challenges for QA systems, especially when QA systems are
trained in a source domain but deployed in a different target domain. In this
work, we investigate the potential benefits of question classification for QA
domain adaptation. We propose a novel framework: Question Classification for
Question Answering (QC4QA). Specifically, a question classifier is adopted to
assign question classes to both the source and target data. Then, we perform
joint training in a self-supervised fashion via pseudo-labeling. For
optimization, inter-domain discrepancy between the source and target domain is
reduced via maximum mean discrepancy (MMD) distance. We additionally minimize
intra-class discrepancy among QA samples of the same question class for
fine-grained adaptation performance. To the best of our knowledge, this is the
first work in QA domain adaptation to leverage question classification with
self-supervised adaptation. We demonstrate the effectiveness of the proposed
QC4QA with consistent improvements against the state-of-the-art baselines on
multiple datasets.Comment: Accepted to COLING 202
Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19
Despite recent progress in improving the performance of misinformation
detection systems, classifying misinformation in an unseen domain remains an
elusive challenge. To address this issue, a common approach is to introduce a
domain critic and encourage domain-invariant input features. However, early
misinformation often demonstrates both conditional and label shifts against
existing misinformation data (e.g., class imbalance in COVID-19 datasets),
rendering such methods less effective for detecting early misinformation. In
this paper, we propose contrastive adaptation network for early misinformation
detection (CANMD). Specifically, we leverage pseudo labeling to generate
high-confidence target examples for joint training with source data. We
additionally design a label correction component to estimate and correct the
label shifts (i.e., class priors) between the source and target domains.
Moreover, a contrastive adaptation loss is integrated in the objective function
to reduce the intra-class discrepancy and enlarge the inter-class discrepancy.
As such, the adapted model learns corrected class priors and an invariant
conditional distribution across both domains for improved estimation of the
target data distribution. To demonstrate the effectiveness of the proposed
CANMD, we study the case of COVID-19 early misinformation detection and perform
extensive experiments using multiple real-world datasets. The results suggest
that CANMD can effectively adapt misinformation detection systems to the unseen
COVID-19 target domain with significant improvements compared to the
state-of-the-art baselines.Comment: Accepted to CIKM 202
Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup
In the real-world application of COVID-19 misinformation detection, a
fundamental challenge is the lack of the labeled COVID data to enable
supervised end-to-end training of the models, especially at the early stage of
the pandemic. To address this challenge, we propose an unsupervised domain
adaptation framework using contrastive learning and adversarial domain mixup to
transfer the knowledge from an existing source data domain to the target
COVID-19 data domain. In particular, to bridge the gap between the source
domain and the target domain, our method reduces a radial basis function (RBF)
based discrepancy between these two domains. Moreover, we leverage the power of
domain adversarial examples to establish an intermediate domain mixup, where
the latent representations of the input text from both domains could be mixed
during the training process. Extensive experiments on multiple real-world
datasets suggest that our method can effectively adapt misinformation detection
systems to the unseen COVID-19 target domain with significant improvements
compared to the state-of-the-art baselines
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