3,511 research outputs found
Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search
Text-based person search aims to retrieve the corresponding person images in
an image database by virtue of a describing sentence about the person, which
poses great potential for various applications such as video surveillance.
Extracting visual contents corresponding to the human description is the key to
this cross-modal matching problem. Moreover, correlated images and descriptions
involve different granularities of semantic relevance, which is usually ignored
in previous methods. To exploit the multilevel corresponding visual contents,
we propose a pose-guided multi-granularity attention network (PMA). Firstly, we
propose a coarse alignment network (CA) to select the related image regions to
the global description by a similarity-based attention. To further capture the
phrase-related visual body part, a fine-grained alignment network (FA) is
proposed, which employs pose information to learn latent semantic alignment
between visual body part and textual noun phrase. To verify the effectiveness
of our model, we perform extensive experiments on the CUHK Person Description
Dataset (CUHK-PEDES) which is currently the only available dataset for
text-based person search. Experimental results show that our approach
outperforms the state-of-the-art methods by 15 \% in terms of the top-1 metric.Comment: published in AAAI2020(oral
Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation
Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24–31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method
RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery
The extraction of sequence patterns from a collection of functionally linked
unlabeled DNA sequences is known as DNA motif discovery, and it is a key task
in computational biology. Several deep learning-based techniques have recently
been introduced to address this issue. However, these algorithms can not be
used in real-world situations because of the need for labeled data. Here, we
presented RL-MD, a novel reinforcement learning based approach for DNA motif
discovery task. RL-MD takes unlabelled data as input, employs a relative
information-based method to evaluate each proposed motif, and utilizes these
continuous evaluation results as the reward. The experiments show that RL-MD
can identify high-quality motifs in real-world data.Comment: This paper is accepted by DSAA2022. The 9th IEEE International
Conference on Data Science and Advanced Analytic
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