437 research outputs found

    Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames

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    This paper makes the first attempt to tackle the challenging task of recovering arbitrary frame rate latent global shutter (GS) frames from two consecutive rolling shutter (RS) frames, guided by the novel event camera data. Although events possess high temporal resolution, beneficial for video frame interpolation (VFI), a hurdle in tackling this task is the lack of paired GS frames. Another challenge is that RS frames are susceptible to distortion when capturing moving objects. To this end, we propose a novel self-supervised framework that leverages events to guide RS frame correction and VFI in a unified framework. Our key idea is to estimate the displacement field (DF) non-linear dense 3D spatiotemporal information of all pixels during the exposure time, allowing for the reciprocal reconstruction between RS and GS frames as well as arbitrary frame rate VFI. Specifically, the displacement field estimation (DFE) module is proposed to estimate the spatiotemporal motion from events to correct the RS distortion and interpolate the GS frames in one step. We then combine the input RS frames and DF to learn a mapping for RS-to-GS frame interpolation. However, as the mapping is highly under-constrained, we couple it with an inverse mapping (i.e., GS-to-RS) and RS frame warping (i.e., RS-to-RS) for self-supervision. As there is a lack of labeled datasets for evaluation, we generate two synthetic datasets and collect a real-world dataset to train and test our method. Experimental results show that our method yields comparable or better performance with prior supervised methods.Comment: This paper has been submitted for review in March 202

    The Research and Development on CTJ10/96-6, the Strong Exhaust Driven by Air Pressure Sanitary Device of the Mine Rescue System

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    According to the technical specification requirements of the mine rescue system, CTJ10/96-6, we design a strong exhaust sanitary device driven by air pressure based on pneumatic control, which adopts a key control by mechanical button. It implements many functions, such as flushing and sealing the smell, measuring and strong exhaust by using mechanical and pneumatic transmission. It also meets the demand of design requirements for the safety and explosion-proof of the mine rescue system.

    What kind of privatization?

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    The Fifteenth Party Congress of the Chinese Communist Party (CCP) attracted worldwide attention by announcing its adoption of "controlling big while releasing the small strategy in reforming its 354,000 state-owned enterprises (SOEs), 240,000 of which are small-sized SOEs. A shareholding system (gufen zhi) will be a major instrument for SOE reform. This paper first discusses the necessity and urgency of SOE reform, and then focuses on the forms of shareholding systems. Finally, it discusses the problems posed by the expansion of government organs and agencies in recent years and the challenges in presents to China's transition

    What kind of privatization?

    Get PDF
    The Fifteenth Party Congress of the Chinese Communist Party (CCP) attracted worldwide attention by announcing its adoption of "controlling big while releasing the small strategy in reforming its 354,000 state-owned enterprises (SOEs), 240,000 of which are small-sized SOEs. A shareholding system (gufen zhi) will be a major instrument for SOE reform. This paper first discusses the necessity and urgency of SOE reform, and then focuses on the forms of shareholding systems. Finally, it discusses the problems posed by the expansion of government organs and agencies in recent years and the challenges in presents to China's transition

    Enhancement of recombinant myricetin on the radiosensitivity of lung cancer A549 and H1299 cells

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    OBJECTIVE: Myricetin, a common dietary flavonoid is widely distributed in fruits and vegetables, and is used as a health food supplement based on its immune function, anti-oxidation, anti-tumor, and anti-inflammatory properties. The aim of this study was to investigate the effects of myricetin on combination with radiotherapy enhance radiosensitivity of lung cancer A549 and H1299 cells. METHODS: A549 cells and H1299 cells were exposed to X-ray with or without myricetin treatment. Colony formation assays, CCK-8 assay, flow cytometry and Caspase-3 level detection were used to evaluate the radiosensitization activity of myricetin on cell proliferation and apoptosis in vitro. Nude mouse tumor xenograft model was built to assessed radiosensitization effect of myricetin in vivo. RESULTS: Compared with the exposed group without myricetin treatment, the groups treated with myricetin showed significantly suppressed cell surviving fraction and proliferation, increased the cell apoptosis and increased Caspase-3 protein expression after X-ray exposure in vitro. And in vivo assay, growth speed of tumor xenografts was significantly decreased in irradiated mice treated with myricetin. CONCLUSIONS: The study demonstrated both in vitro and in vivo evidence that combination of myricetin with radiotherapy can enhance tumor radiosensitivity of pulmonary carcinoma A549 and H1299 cells, and myricetin could be a potential radiosensitizer for lung cancer therapy. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/579151800121063

    New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning

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    To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the online class incremental learning (CIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world. Actually, the CIL faces a stability-plasticity dilemma, where the stability means the ability to preserve old knowledge while the plasticity denotes the ability to incorporate new knowledge. Although replay-based methods have shown exceptional promise, most of them concentrate on the strategy for updating and retrieving memory to keep stability at the expense of plasticity. To strike a preferable trade-off between stability and plasticity, we propose a Adaptive Focus Shifting algorithm (AFS), which dynamically adjusts focus to ambiguous samples and non-target logits in model learning. Through a deep analysis of the task-recency bias caused by class imbalance, we propose a revised focal loss to mainly keep stability. By utilizing a new weight function, the revised focal loss can pay more attention to current ambiguous samples, which can provide more information of the classification boundary. To promote plasticity, we introduce a virtual knowledge distillation. By designing a virtual teacher, it assigns more attention to non-target classes, which can surmount overconfidence and encourage model to focus on inter-class information. Extensive experiments on three popular datasets for CIL have shown the effectiveness of AFS. The code will be available at \url{https://github.com/czjghost/AFS}.Comment: 12 pages,15 figure

    Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features

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    Non-exemplar class-incremental learning refers to classifying new and old classes without storing samples of old classes. Since only new class samples are available for optimization, it often occurs catastrophic forgetting of old knowledge. To alleviate this problem, many new methods are proposed such as model distillation, class augmentation. In this paper, we propose an effective non-exemplar method called RAMF consisting of Random Auxiliary classes augmentation and Mixed Feature. On the one hand, we design a novel random auxiliary classes augmentation method, where one augmentation is randomly selected from three augmentations and applied on the input to generate augmented samples and extra class labels. By extending data and label space, it allows the model to learn more diverse representations, which can prevent the model from being biased towards learning task-specific features. When learning new tasks, it will reduce the change of feature space and improve model generalization. On the other hand, we employ mixed feature to replace the new features since only using new feature to optimize the model will affect the representation that was previously embedded in the feature space. Instead, by mixing new and old features, old knowledge can be retained without increasing the computational complexity. Extensive experiments on three benchmarks demonstrate the superiority of our approach, which outperforms the state-of-the-art non-exemplar methods and is comparable to high-performance replay-based methods.Comment: 12 pages, 7 figure
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