175 research outputs found
Learning Fashion Compatibility with Bidirectional LSTMs
The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.Comment: ACM MM 1
VITON: An Image-based Virtual Try-on Network
We present an image-based VIirtual Try-On Network (VITON) without using 3D
information in any form, which seamlessly transfers a desired clothing item
onto the corresponding region of a person using a coarse-to-fine strategy.
Conditioned upon a new clothing-agnostic yet descriptive person representation,
our framework first generates a coarse synthesized image with the target
clothing item overlaid on that same person in the same pose. We further enhance
the initial blurry clothing area with a refinement network. The network is
trained to learn how much detail to utilize from the target clothing item, and
where to apply to the person in order to synthesize a photo-realistic image in
which the target item deforms naturally with clear visual patterns. Experiments
on our newly collected Zalando dataset demonstrate its promise in the
image-based virtual try-on task over state-of-the-art generative models
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective
As a popular concept proposed in the field of psychology, affordance has been
regarded as one of the important abilities that enable humans to understand and
interact with the environment. Briefly, it captures the possibilities and
effects of the actions of an agent applied to a specific object or, more
generally, a part of the environment. This paper provides a short review of the
recent developments of deep robotic affordance learning (DRAL), which aims to
develop data-driven methods that use the concept of affordance to aid in
robotic tasks. We first classify these papers from a reinforcement learning
(RL) perspective, and draw connections between RL and affordances. The
technical details of each category are discussed and their limitations
identified. We further summarise them and identify future challenges from the
aspects of observations, actions, affordance representation, data-collection
and real-world deployment. A final remark is given at the end to propose a
promising future direction of the RL-based affordance definition to include the
predictions of arbitrary action consequences.Comment: This paper is under revie
Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning
Although Deep Reinforcement Learning (DRL) has been popular in many
disciplines including robotics, state-of-the-art DRL algorithms still struggle
to learn long-horizon, multi-step and sparse reward tasks, such as stacking
several blocks given only a task-completion reward signal. To improve learning
efficiency for such tasks, this paper proposes a DRL exploration technique,
termed A^2, which integrates two components inspired by human experiences:
Abstract demonstrations and Adaptive exploration. A^2 starts by decomposing a
complex task into subtasks, and then provides the correct orders of subtasks to
learn. During training, the agent explores the environment adaptively, acting
more deterministically for well-mastered subtasks and more stochastically for
ill-learnt subtasks. Ablation and comparative experiments are conducted on
several grid-world tasks and three robotic manipulation tasks. We demonstrate
that A^2 can aid popular DRL algorithms (DQN, DDPG, and SAC) to learn more
efficiently and stably in these environments.Comment: Accepted by The 27th IEEE International Conference on Automation and
Computing (ICAC2022
Assessing Callous-Unemotional Traits in Chinese Detained Boys: Factor Structure and Construct Validity of the Inventory of Callous-Unemotional Traits
The Inventory of Callous-Unemotional Traits (ICU) was designed to evaluate multiple facets of Callous-Unemotional (CU) traits in youths. However, no study has examined the factor structure and psychometrical properties of the ICU in Chinese detained juveniles. The current study assesses the factor structure, internal consistency and convergent validity of the ICU in 613 Chinese detained boys. Confirmatory factor analysis results indicated that the original three-factor model with 24 items showed an unacceptable fit to the data, however, the 11-item shortened version of the ICU (ICU-11) with callousness and uncaring dimensions showed the best fit. Moreover, the ICU-11 total score and factor scores had good and acceptable internal consistencies. The convergent and criterion validity of the ICU-11 was demonstrated by comparable and significant associations in the expected direction with relevant external criteria (e.g., psychopathy, aggression, and empathy). In conclusion, present findings indicated that the ICU-11 is a reliable and efficient instrument to replace the original ICU when assessing CU traits in the Chinese male detained juvenile sample.This work was supported by the National Natural Science
Foundation of China (Grant Nos. 31800945 and 31400904) and
Guangzhou University’s 2017 training program for young topnotch personnels (BJ201715)
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