411 research outputs found
Exploiting Visual Semantic Reasoning for Video-Text Retrieval
Video retrieval is a challenging research topic bridging the vision and
language areas and has attracted broad attention in recent years. Previous
works have been devoted to representing videos by directly encoding from
frame-level features. In fact, videos consist of various and abundant semantic
relations to which existing methods pay less attention. To address this issue,
we propose a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit
reasoning between frame regions. Specifically, we consider frame regions as
vertices and construct a fully-connected semantic correlation graph. Then, we
perform reasoning by novel random walk rule-based graph convolutional networks
to generate region features involved with semantic relations. With the benefit
of reasoning, semantic interactions between regions are considered, while the
impact of redundancy is suppressed. Finally, the region features are aggregated
to form frame-level features for further encoding to measure video-text
similarity. Extensive experiments on two public benchmark datasets validate the
effectiveness of our method by achieving state-of-the-art performance due to
the powerful semantic reasoning.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International
Joint Conferences on Artificial Intelligence), all rights reserved.
http://static.ijcai.org/2020-accepted_papers.htm
Risk factors for surgical site infection of pilon fractures
OBJECTIVES: Pilon fracture is a complex injury that is often associated with severe soft tissue damage and high rates of surgical site infection. The goal of this study was to analyze and identify independent risk factors for surgical site infection among patients undergoing surgical fixation of a pilon fracture. METHODS: The medical records of all pilon fracture patients who underwent surgical fixation from January 2010 to October 2012 were reviewed to identify those who developed a surgical site infection. Then, we constructed univariate and multivariate logistic regressions to evaluate the independent associations of potential risk factors with surgical site infection in patients undergoing surgical fixation of a pilon fracture. RESULTS: A total of 519 patients were enrolled in the study from January 2010 to October 2012. A total of 12 of the 519 patients developed a surgical site infection, for an incidence of 2.3%. These patients were followed for 12 to 29 months, with an average follow-up period of 19.1 months. In the final regression model, open fracture, elevated postoperative glucose levels (≥125 mg/dL), and a surgery duration of more than 150 minutes were significant risk factors for surgical site infection following surgical fixation of a pilon fracture. CONCLUSIONS: Open fractures, elevated postoperative glucose levels (≥125 mg/dL), and a surgery duration of more than 150 minutes were related to an increased risk for surgical site infection following surgical fixation of a pilon fracture. Patients exhibiting the risk factors identified in this study should be counseled regarding the possible surgical site infection that may develop after surgical fixation
Tensile fracture behavior of high carbon high manganese steel with single-phase austenite structure
344-347The microstructure, fracture morphology and tensile fracture process of high manganese steel with single-phase austenite after water toughening have been investigated by optical microscope (OM), scanning electron microscope (SEM) and transmission election microscope (TEM). The results have shown a large number of deformation twins forming within the austenite matrix after tensile fracture, which have been parallel with and cross to each other. The fracture surface has in the shape of dimple and parallel cascade steps inside the larger size. There has been existed many Shockley partial dislocations, stacking faults and dislocation loops at twin-twin intersections. We have discovered like-microvoid accumulation fracture caused by non-second-phase particles in high manganese steel with single-phase austenite, and the reasons for its formation have been discussed in this paper
Deep Domain Adaptation for Pavement Crack Detection
Deep learning-based pavement cracks detection methods often require
large-scale labels with detailed crack location information to learn accurate
predictions. In practice, however, crack locations are very difficult to be
manually annotated due to various visual patterns of pavement crack. In this
paper, we propose a Deep Domain Adaptation-based Crack Detection Network
(DDACDN), which learns to take advantage of the source domain knowledge to
predict the multi-category crack location information in the target domain,
where only image-level labels are available. Specifically, DDACDN first
extracts crack features from both the source and target domain by a two-branch
weights-shared backbone network. And in an effort to achieve the cross-domain
adaptation, an intermediate domain is constructed by aggregating the
three-scale features from the feature space of each domain to adapt the crack
features from the source domain to the target domain. Finally, the network
involves the knowledge of both domains and is trained to recognize and localize
pavement cracks. To facilitate accurate training and validation for domain
adaptation, we use two challenging pavement crack datasets CQU-BPDD and
RDD2020. Furthermore, we construct a new large-scale Bituminous Pavement
Multi-label Disease Dataset named CQU-BPMDD, which contains 38994
high-resolution pavement disease images to further evaluate the robustness of
our model. Extensive experiments demonstrate that DDACDN outperforms
state-of-the-art pavement crack detection methods in predicting the crack
location on the target domain.Comment: 12 pages, 10 figure
- …