3,386 research outputs found

    Temporal Action Detection with Structured Segment Networks

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    Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we present the structured segment network (SSN), a novel framework which models the temporal structure of each action instance via a structured temporal pyramid. On top of the pyramid, we further introduce a decomposed discriminative model comprising two classifiers, respectively for classifying actions and determining completeness. This allows the framework to effectively distinguish positive proposals from background or incomplete ones, thus leading to both accurate recognition and localization. These components are integrated into a unified network that can be efficiently trained in an end-to-end fashion. Additionally, a simple yet effective temporal action proposal scheme, dubbed temporal actionness grouping (TAG) is devised to generate high quality action proposals. On two challenging benchmarks, THUMOS14 and ActivityNet, our method remarkably outperforms previous state-of-the-art methods, demonstrating superior accuracy and strong adaptivity in handling actions with various temporal structures.Comment: To appear in ICCV2017. Code & models available at http://yjxiong.me/others/ss

    l-Peptide functionalized dual-responsive nanoparticles for controlled paclitaxel release and enhanced apoptosis in breast cancer cells

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    Nanoparticles and macromolecular carriers have been widely used to increase the efficacy of chemotherapeutics, largely through passive accumulation provided by their enhanced permeability and retention effect. However, the therapeutic efficacy of nanoscale anticancer drug delivery systems is severely truncated by their low tumor-targetability and inefficient drug release at the target site. Here, the design and development of novel l-peptide functionalized dual-responsive nanoparticles (l-CS-g-PNIPAM-PTX) for active targeting and effective treatment of GRP78-overexpressing human breast cancer in vitro and in vivo are reported. l-CS-g-PNIPAM-PTX NPs have a relative high drug loading (13.5%) and excellent encapsulation efficiency (74.3%) and an average diameter of 275 nm. The release of PTX is slow at pH 7.4 and 25 °C but greatly accelerated at pH 5.0 and 37 °C. MTT assays and confocal experiments showed that the l-CS-g-PNIPAM-PTX NPs possessed high targetability and antitumor activity toward GRP78 overexpressing MDA-MB-231 human breast cancer cells. As expected, l-CS-g-PNIPAM-PTX NPs could effectively treat mice bearing MDA-MB-231 human breast tumor xenografts with little side effects, resulting in complete inhibition of tumor growth and a high survival rate over an experimental period of 60 days. These results indicate that l-peptide-functionalized acid - and thermally activated - PTX prodrug NPs have a great potential for targeted chemotherapy in breast cancer.</p

    A fast algorithm for the computation of 2-D forward and inverse MDCT

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    International audienceA fast algorithm for computing the two-dimensional (2-D) forward and inverse modified discrete cosine transform (MDCT and IMDCT) is proposed. The algorithm converts the 2-D MDCT and IMDCT with block size M N into four 2-D discrete cosine transforms (DCTs) with block size ðM=4Þ ðN=4Þ. It is based on an algorithm recently presented by Cho et al. [An optimized algorithm for computing the modified discrete cosine transform and its inverse transform, in: Proceedings of the IEEE TENCON, vol. A, 21–24 November 2004, pp. 626–628] for the efficient calculation of onedimensional MDCT and IMDCT. Comparison of the computational complexity with the traditional row–column method shows that the proposed algorithm reduces significantly the number of arithmetic operations
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