333,019 research outputs found
MGCN: Medical Relation Extraction Based on GCN
With the progress of society and the improvement of living standards, people pay more and more attention to personal health, and WITMED (Wise Information Technology of med) has occupied an important position. The relationship prediction work in the medical field has high requirements on the interpretability of the method, but the relationship between medical entities is complex, and the existing methods are difficult to meet the requirements. This paper proposes a novel medical information relation extraction method MGCN, which combines contextual information to provide global interpretability for relation prediction of medical entities. The method uses Co-occurrence Graph and Graph Convolutional Network to build up a network of relations between entities, uses the Open-world Assumption to construct potential relations between associated entities, and goes through the Knowledge-aware Attention mechanism to give relation prediction for the entity pair of interest. Experiments were conducted on a public medical dataset CTF, MGCN achieved the score of 0.831, demonstrating its effectiveness in medical relation extraction
Similarity Reasoning and Filtration for Image-Text Matching
Image-text matching plays a critical role in bridging the vision and
language, and great progress has been made by exploiting the global alignment
between image and sentence, or local alignments between regions and words.
However, how to make the most of these alignments to infer more accurate
matching scores is still underexplored. In this paper, we propose a novel
Similarity Graph Reasoning and Attention Filtration (SGRAF) network for
image-text matching. Specifically, the vector-based similarity representations
are firstly learned to characterize the local and global alignments in a more
comprehensive manner, and then the Similarity Graph Reasoning (SGR) module
relying on one graph convolutional neural network is introduced to infer
relation-aware similarities with both the local and global alignments. The
Similarity Attention Filtration (SAF) module is further developed to integrate
these alignments effectively by selectively attending on the significant and
representative alignments and meanwhile casting aside the interferences of
non-meaningful alignments. We demonstrate the superiority of the proposed
method with achieving state-of-the-art performances on the Flickr30K and MSCOCO
datasets, and the good interpretability of SGR and SAF modules with extensive
qualitative experiments and analyses.Comment: 14 pages, 8 figures, Accepted by AAAI202
Context-Aware Transformer for 3D Point Cloud Automatic Annotation
3D automatic annotation has received increased attention since manually
annotating 3D point clouds is laborious. However, existing methods are usually
complicated, e.g., pipelined training for 3D foreground/background
segmentation, cylindrical object proposals, and point completion. Furthermore,
they often overlook the inter-object feature relation that is particularly
informative to hard samples for 3D annotation. To this end, we propose a simple
yet effective end-to-end Context-Aware Transformer (CAT) as an automated 3D-box
labeler to generate precise 3D box annotations from 2D boxes, trained with a
small number of human annotations. We adopt the general encoder-decoder
architecture, where the CAT encoder consists of an intra-object encoder (local)
and an inter-object encoder (global), performing self-attention along the
sequence and batch dimensions, respectively. The former models intra-object
interactions among points, and the latter extracts feature relations among
different objects, thus boosting scene-level understanding. Via local and
global encoders, CAT can generate high-quality 3D box annotations with a
streamlined workflow, allowing it to outperform existing state-of-the-art by up
to 1.79% 3D AP on the hard task of the KITTI test set
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
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