928 research outputs found
Chronic-Pain Protective Behavior Detection with Deep Learning
In chronic pain rehabilitation, physiotherapists adapt physical activity to
patients' performance based on their expression of protective behavior,
gradually exposing them to feared but harmless and essential everyday
activities. As rehabilitation moves outside the clinic, technology should
automatically detect such behavior to provide similar support. Previous works
have shown the feasibility of automatic protective behavior detection (PBD)
within a specific activity. In this paper, we investigate the use of deep
learning for PBD across activity types, using wearable motion capture and
surface electromyography data collected from healthy participants and people
with chronic pain. We approach the problem by continuously detecting protective
behavior within an activity rather than estimating its overall presence. The
best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross
validation. When protective behavior is modelled per activity type, performance
is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for
sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This
performance reaches excellent level of agreement with the average experts'
rating performance suggesting potential for personalized chronic pain
management at home. We analyze various parameters characterizing our approach
to understand how the results could generalize to other PBD datasets and
different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on
Computing for Healthcar
Linguistic Structure Guided Context Modeling for Referring Image Segmentation
Referring image segmentation aims to predict the foreground mask of the
object referred by a natural language sentence. Multimodal context of the
sentence is crucial to distinguish the referent from the background. Existing
methods either insufficiently or redundantly model the multimodal context. To
tackle this problem, we propose a "gather-propagate-distribute" scheme to model
multimodal context by cross-modal interaction and implement this scheme as a
novel Linguistic Structure guided Context Modeling (LSCM) module. Our LSCM
module builds a Dependency Parsing Tree suppressed Word Graph (DPT-WG) which
guides all the words to include valid multimodal context of the sentence while
excluding disturbing ones through three steps over the multimodal feature,
i.e., gathering, constrained propagation and distributing. Extensive
experiments on four benchmarks demonstrate that our method outperforms all the
previous state-of-the-arts.Comment: Accepted by ECCV 2020. Code is available at
https://github.com/spyflying/LSCM-Refse
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Referring Camouflaged Object Detection
In this paper, we consider the problem of referring camouflaged object
detection (Ref-COD), a new task that aims to segment specified camouflaged
objects based on some form of reference, e.g., image, text. We first assemble a
large-scale dataset, called R2C7K, which consists of 7K images covering 64
object categories in real-world scenarios. Then, we develop a simple but strong
dual-branch framework, dubbed R2CNet, with a reference branch learning common
representations from the referring information and a segmentation branch
identifying and segmenting camouflaged objects under the guidance of the common
representations. In particular, we design a Referring Mask Generation module to
generate pixel-level prior mask and a Referring Feature Enrichment module to
enhance the capability of identifying camouflaged objects. Extensive
experiments show the superiority of our Ref-COD methods over their COD
counterparts in segmenting specified camouflaged objects and identifying the
main body of target objects. Our code and dataset are publicly available at
https://github.com/zhangxuying1004/RefCOD
- …