Video anomaly detection (VAD) is a demanding task because the very definition of anomalies in videos is inherently inconclusive and also due to the high manpower required to supervise lengthy videos. This research pap...
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Stock Portfolio management involves managing the buying, holding and selling decisions for the various stocks in the portfolio. There has been work where Reinforcement learning (RL) based actor-critic methods like Dee...
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The rise of large language models (LLMs) like Chat-GPT has significantly transformed the field of natural language processing (NLP). These models are now central to many companies' operations due to their capabili...
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In today's world, the UGC (User Generated Contents) videos have increased exponentially. Billions of videos are uploaded, played and exchanged between different actors. In this context, automatic video content cla...
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Volume of video content surpass all other content types in internet. As per the reports from different sources, video traffic had acquired 82% of internet usage in 2022. Video is going to be more important in the year...
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ISBN:
(纸本)9781665455725
Volume of video content surpass all other content types in internet. As per the reports from different sources, video traffic had acquired 82% of internet usage in 2022. Video is going to be more important in the years to come for user engagement, advertisement & marketing, news, education etc. Video information retrieval becomes an important problem to solve in this context. An accurate and fast video tagging system can aid a good content recommendation to the end users. It helps to audit the content automatically thereby platforms can control the contents which are politically and morally harmful. There are not many faster or cost-effective mechanisms to tag user generated videos at this moment. Manual tagging is a costly and highly time taking task. A delay in indexing the videos like news, sports etc., shall reduce its freshness and relevancy. Deep learning techniques have reached its maturity in the contents like text and images, but it is not the case with videos. Deep learning models need more resources to deal with videos due to its multi-modality nature, and temporal behavior. Apart from that, there are not many large-scale video datasets available at this moment. Youtube-8M is the largest dataset which is publicly available as of now. Much research works happened over Youtube-8M dataset. From our study, all these have a potential limitation. For example, in Youtube-8M, Video labels are only around 3.8K which are not covering all real-world tags. It is not covering the new domains which are created along with the surge in the content traffic. This study aims to handle this problem of tag creation through different methods available thereby enhancing the labels to a much wider set. This work also aims to produce a scalable tagging pipeline which uses multiple retrieval mechanisms, combine their results. The work aims to standardize the retrieved tokens across languages. This work creates a dataset as an outcome from 'Wikidata', which can be used for any NLP
This paper presents a comprehensive study on speech enhancement (SE) techniques, particularly focusing on the utilization of the discrete cosine transform (DCT) in the modulation domain (MD) in combination with the mi...
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This study adopts an empirical approach to evaluate the efficacy of image processing methods in conservation efforts for animals. The initial phase involves the collection of data from various sources within the natur...
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Handling missing data is crucial in machinelearning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature...
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We introduce a method for partitioning a time series into segments. The method extends the recently introduced Fast Low-Cost Semantic Segmentation (FLUSS) algorithm to increase its robustness against noise and to auto...
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We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a ...
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We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product ***, our loss function has an intimate connection with the steady entropy production rate(EPR),enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and the state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an eight-dimensional(8D)limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.
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