Recently visual saliency has attracted wide attention of researchers in the computer vision and multimedia field. However, most of the visual saliency-related research was conducted on still images for studying static saliency. In this paper, we give a comprehensive comparative study for the first time of dynamic saliency (video shots) and static saliency (key frames of the corresponding video shots), and two key observations are obtained: 1) video saliency is often different from, yet quite related with, image saliency, and 2) camera motions, such as tilting, panning or zooming, affect dynamic saliency significantly. Motivated by these observations, we propose a novel camera motion and image saliency aware model for dynamic saliency prediction. The extensive experiments on two static-vs-dynamic saliency datasets collected by us show that our proposed method outperforms the state-of-the-art methods for dynamic saliency prediction. Finally, we also introduce the application of dynamic saliency prediction for dynamic video captioning, assisting people with hearing impairments to better entertain videos with only off-screen voices, e.g., documentary films, news videos and sports videos.
Fundamental physics under the surface plasmon (SP) of graphene and the functional application beyond ultraviolet (UV) lasing of ZnO are both fascinating research areas. Herein, the optical field confinement induced by graphene SP was simulated theoretically in a graphene-coated ZnO microrod, which acted as a whispering-gallery microcavity for lasing resonance. Distinct optical field confinement and photoluminescence (PL) enhancement were observed experimentally. Stable and transient spectra were employed to analyze the PL enhancement and the coupling dynamics between graphene SP and ZnO interband emission. As a functional application, the graphene-coated ZnO microcavities presented the obviously improved whispering-gallery mode (WGM) lasing performance. These results would be valuable for designing novel optical and photoelectronic devices based on SP coupling in graphene-semiconductor hybrid materials.
VD-TSL could increase drug efficacy and decrease system toxicity, by making good use of synergism of VCR and DOX, as well as high targeting efficiency of TSL.
Small object detection is a very challenging yet practical vision task. With deep network-based methods, the contextual information of small objects may disappear when the network goes deeper. An intuitive solution to alleviate this issue is to increase the input resolution, however, it will aggravate the large variant of object scale and introduce unbearable computation cost. To leverage the benefits of high-resolution images without bringing up new problems, we propose a High-Resolution Detection Network (HRDNet) which takes multiple resolution inputs with multi-depth backbones. Meanwhile, we propose the Multi-Depth Image Pyramid Network (MD-IPN) and Multi-Scale Feature Pyramid Network (MS-FPN). The MD-IPN maintains multiple position information using multiple depth backbones. Specifically, high-resolution input will be fed into a shallow network to reserve more positional information and reduce computational costs, while low-resolution input will be fed into a deep network to extract more semantics. By extracting various features from high to low resolutions, the MD-IPN can improve the performance of small object detection and maintain the performance of middle and large objects. Additionally, MS-FPN is introduced to align and fuse multi-scale feature groups generated by MD-IPN to reduce the information imbalance. Extensive experiments are conducted on the COCO2017 and the typical small object dataset, VisDrone 2019. Notably, our HRDNet achieves the state-of-the-art on these two datasets with significant improvements on small objects.
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