77 research outputs found

    DoubleHigherNet : coarse-to-fine precise heatmap bottom-up dynamic pose computer intelligent estimation

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    Accurate keypoint positioning is necessary for bottom-up multi-person pose estimation methods to handle scale variation and crowdedness. In this paper, we present DoubleHigherNet: a novel network learning scale-aware and precise heatmap representation for bottom-up process using double high-resolution feature pyramids and coarse-to-fine training. The two feature pyramids in DoubleHigherNet consists of 1/4 resolution feature and higher-resolution (1/2) maps generated by attention fusion blocks and transposed convolutions. Benefited by the training strategy, muti-resoltion and coarse-fine heatmap aggregation, the proposed approach is able to predict keypoints more accurately so as to perform better on difficult crowded scenes. DoubleHigherNetw32 achieves competitive result on CrowdPose-test, surpassing all the top-down methods and bottom-up SOTA HigherHRNet-w32 (which possesses similar number of params with DoubleHigherNet-w32)

    A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods

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    Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences. Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks. In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training. We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic.Comment: Accepted to EACL 2023 as regular long pape

    Laser directed writing of flat lenses on buckypaper

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    Laser directed patterning of carbon nanotubes-based buckypaper for producing a diffractive optical device is presented here.</p

    Public involvement in setting a national research agenda

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    <p>(A) Graphical map of the BLAST results showing nucleotide identity between <i>A</i>. <i>fasciata</i> mitogenome and 15 related species listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136297#pone.0136297.t001" target="_blank">Table 1</a>, as generated by the CGView comparison tool (CCT). CCT arranges BLAST result in an order where sequence that is most similar to the reference (<i>A</i>. <i>fasciata</i>) is placed closer to the outer edge of the map. The rings labelled 1 to17 indicate BLAST results of <i>A</i>. <i>fasciata</i> mitogenome against <i>A</i>. <i>chrysaetos</i>, <i>N</i>. <i>nipalensis</i>, <i>N</i>. <i>alboniger</i>, <i>S</i>. <i>cheela</i>, <i>A</i>. <i>monachus</i>, <i>B</i>. <i>lagopus</i>, <i>B</i>. <i>buteo</i>, <i>B</i>. <i>buteo burmanicus</i>, <i>A</i>. <i>soloensis</i>, <i>A</i>. <i>virgatus</i>, <i>A</i>. <i>gentilis</i>, <i>A</i>. <i>nisus</i>, <i>P</i>. <i>haliaetus</i>, <i>S</i>. <i>serpentarius</i>, <i>C</i>. <i>aura</i>, <i>P</i>. <i>badius</i>, and <i>S</i>. <i>leptogrammica</i>, respectively. (B) Nucleotide-based phylogenetic tree of 16 Accipitriformes species, with two Strigiformes birds as outgroups. This analysis is based on 13PCGs. Both ML and Bayesian analyses produced identical tree topologies. The ML bootstrap and Bayesian posterior probability values for each node are indicated.</p
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