10,476 research outputs found
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation
Context modeling is essential to generate coherent and consistent translation
for Document-level Neural Machine Translations. The widely used method for
document-level translation usually compresses the context information into a
representation via hierarchical attention networks. However, this method
neither considers the relationship between context words nor distinguishes the
roles of context words. To address this problem, we propose a query-guided
capsule networks to cluster context information into different perspectives
from which the target translation may concern. Experiment results show that our
method can significantly outperform strong baselines on multiple data sets of
different domains.Comment: 11 pages, 7 figures, 2019 Conference on Empirical Methods in Natural
Language Processin
Advancement Auto-Assessment of Students Knowledge States from Natural Language Input
Knowledge Assessment is a key element in adaptive instructional systems and in particular in Intelligent Tutoring Systems because fully adaptive tutoring presupposes accurate assessment. However, this is a challenging research problem as numerous factors affect students’ knowledge state estimation such as the difficulty level of the problem, time spent in solving the problem, etc. In this research work, we tackle this research problem from three perspectives: assessing the prior knowledge of students, assessing the natural language short and long students’ responses, and knowledge tracing.Prior knowledge assessment is an important component of knowledge assessment as it facilitates the adaptation of the instruction from the very beginning, i.e., when the student starts interacting with the (computer) tutor. Grouping students into groups with similar mental models and patterns of prior level of knowledge allows the system to select the right level of scaffolding for each group of students. While not adapting instruction to each individual learner, the advantage of adapting to groups of students based on a limited number of prior knowledge levels has the advantage of decreasing the authoring costs of the tutoring system. To achieve this goal of identifying or clustering students based on their prior knowledge, we have employed effective clustering algorithms. Automatically assessing open-ended student responses is another challenging aspect of knowledge assessment in ITSs. In dialogue-based ITSs, the main interaction between the learner and the system is natural language dialogue in which students freely respond to various system prompts or initiate dialogue moves in mixed-initiative dialogue systems. Assessing freely generated student responses in such contexts is challenging as students can express the same idea in different ways owing to different individual style preferences and varied individual cognitive abilities. To address this challenging task, we have proposed several novel deep learning models as they are capable to capture rich high-level semantic features of text. Knowledge tracing (KT) is an important type of knowledge assessment which consists of tracking students’ mastery of knowledge over time and predicting their future performances. Despite the state-of-the-art results of deep learning in this task, it has many limitations. For instance, most of the proposed methods ignore pertinent information (e.g., Prior knowledge) that can enhance the knowledge tracing capability and performance. Working toward this objective, we have proposed a generic deep learning framework that accounts for the engagement level of students, the difficulty of questions and the semantics of the questions and uses a novel times series model called Temporal Convolutional Network for future performance prediction. The advanced auto-assessment methods presented in this dissertation should enable better ways to estimate learner’s knowledge states and in turn the adaptive scaffolding those systems can provide which in turn should lead to more effective tutoring and better learning gains for students. Furthermore, the proposed method should enable more scalable development and deployment of ITSs across topics and domains for the benefit of all learners of all ages and backgrounds
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Identifying mix-and-match relationships between fashion items is an urgent
task in a fashion e-commerce recommender system. It will significantly enhance
user experience and satisfaction. However, due to the challenges of inferring
the rich yet complicated set of compatibility patterns in a large e-commerce
corpus of fashion items, this task is still underexplored. Inspired by the
recent advances in multi-relational knowledge representation learning and deep
neural networks, this paper proposes a novel Translation-based Neural Fashion
Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion
item embeddings and category-specific complementary relations in a unified
space via an end-to-end learning manner. TransNFCM places items in a unified
embedding space where a category-specific relation (category-comp-category) is
modeled as a vector translation operating on the embeddings of compatible items
from the corresponding categories. By this way, we not only capture the
specific notion of compatibility conditioned on a specific pair of
complementary categories, but also preserve the global notion of compatibility.
We also design a deep fashion item encoder which exploits the complementary
characteristic of visual and textual features to represent the fashion
products. To the best of our knowledge, this is the first work that uses
category-specific complementary relations to model the category-aware
compatibility between items in a translation-based embedding space. Extensive
experiments demonstrate the effectiveness of TransNFCM over the
state-of-the-arts on two real-world datasets.Comment: Accepted in AAAI 2019 conferenc
- …