142 research outputs found
Age Progression and Regression with Spatial Attention Modules
Age progression and regression refers to aesthetically render-ing a given
face image to present effects of face aging and rejuvenation, respectively.
Although numerous studies have been conducted in this topic, there are two
major problems: 1) multiple models are usually trained to simulate different
age mappings, and 2) the photo-realism of generated face images is heavily
influenced by the variation of training images in terms of pose, illumination,
and background. To address these issues, in this paper, we propose a framework
based on conditional Generative Adversarial Networks (cGANs) to achieve age
progression and regression simultaneously. Particularly, since face aging and
rejuvenation are largely different in terms of image translation patterns, we
model these two processes using two separate generators, each dedicated to one
age changing process. In addition, we exploit spatial attention mechanisms to
limit image modifications to regions closely related to age changes, so that
images with high visual fidelity could be synthesized for in-the-wild cases.
Experiments on multiple datasets demonstrate the ability of our model in
synthesizing lifelike face images at desired ages with personalized features
well preserved, and keeping age-irrelevant regions unchanged
Promoting Open-domain Dialogue Generation through Learning Pattern Information between Contexts and Responses
Recently, utilizing deep neural networks to build the opendomain dialogue
models has become a hot topic. However, the responses generated by these models
suffer from many problems such as responses not being contextualized and tend
to generate generic responses that lack information content, damaging the
user's experience seriously. Therefore, many studies try introducing more
information into the dialogue models to make the generated responses more vivid
and informative. Unlike them, this paper improves the quality of generated
responses by learning the implicit pattern information between contexts and
responses in the training samples. In this paper, we first build an open-domain
dialogue model based on the pre-trained language model (i.e., GPT-2). And then,
an improved scheduled sampling method is proposed for pre-trained models, by
which the responses can be used to guide the response generation in the
training phase while avoiding the exposure bias problem. More importantly, we
design a response-aware mechanism for mining the implicit pattern information
between contexts and responses so that the generated replies are more diverse
and approximate to human replies. Finally, we evaluate the proposed model (RAD)
on the Persona-Chat and DailyDialog datasets; and the experimental results show
that our model outperforms the baselines on most automatic and manual metrics
Spatial Transform Decoupling for Oriented Object Detection
Vision Transformers (ViTs) have achieved remarkable success in computer
vision tasks. However, their potential in rotation-sensitive scenarios has not
been fully explored, and this limitation may be inherently attributed to the
lack of spatial invariance in the data-forwarding process. In this study, we
present a novel approach, termed Spatial Transform Decoupling (STD), providing
a simple-yet-effective solution for oriented object detection with ViTs. Built
upon stacked ViT blocks, STD utilizes separate network branches to predict the
position, size, and angle of bounding boxes, effectively harnessing the spatial
transform potential of ViTs in a divide-and-conquer fashion. Moreover, by
aggregating cascaded activation masks (CAMs) computed upon the regressed
parameters, STD gradually enhances features within regions of interest (RoIs),
which complements the self-attention mechanism. Without bells and whistles, STD
achieves state-of-the-art performance on the benchmark datasets including
DOTA-v1.0 (82.24% mAP) and HRSC2016 (98.55% mAP), which demonstrates the
effectiveness of the proposed method. Source code is available at
https://github.com/yuhongtian17/Spatial-Transform-Decoupling
Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation
One-shot face re-enactment is a challenging task due to the identity mismatch
between source and driving faces. Specifically, the suboptimally disentangled
identity information of driving subjects would inevitably interfere with the
re-enactment results and lead to face shape distortion. To solve this problem,
this paper proposes to use 3D Morphable Model (3DMM) for explicit facial
semantic decomposition and identity disentanglement. Instead of using 3D
coefficients alone for re-enactment control, we take the advantage of the
generative ability of 3DMM to render textured face proxies. These proxies
contain abundant yet compact geometric and semantic information of human faces,
which enable us to compute the face motion field between source and driving
images by estimating the dense correspondence. In this way, we could
approximate re-enactment results by warping source images according to the
motion field, and a Generative Adversarial Network (GAN) is adopted to further
improve the visual quality of warping results. Extensive experiments on various
datasets demonstrate the advantages of the proposed method over existing
start-of-the-art benchmarks in both identity preservation and re-enactment
fulfillment
Many Ways to Be Lonely: Fine-Grained Characterization of Loneliness and Its Potential Changes in COVID-19
Loneliness has been associated with negative outcomes for physical and mental
health. Understanding how people express and cope with various forms of
loneliness is critical for early screening and targeted interventions to reduce
loneliness, particularly among vulnerable groups such as young adults. To
examine how different forms of loneliness and coping strategies manifest in
loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained
Loneliness) by using Reddit posts in two young adult-focused forums and two
loneliness related forums consisting of a diverse age group. We provided
annotations by trained human annotators for binary and fine-grained loneliness
classifications of the posts. Trained on FIG-Loneliness, two BERT-based models
were used to understand loneliness forms and authors' coping strategies in
these forums. Our binary loneliness classification achieved an accuracy above
97%, and fine-grained loneliness category classification reached an average
accuracy of 77% across all labeled categories. With FIG-Loneliness and model
predictions, we found that loneliness expressions in the young adults related
forums were distinct from other forums. Those in young adult-focused forums
were more likely to express concerns pertaining to peer relationship, and were
potentially more sensitive to geographical isolation impacted by the COVID-19
pandemic lockdown. Also, we showed that different forms of loneliness have
differential use in coping strategies
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