6,340 research outputs found

    Joint Video and Text Parsing for Understanding Events and Answering Queries

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    We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results

    Automated Tool To Generate Global Clock Distribution For Spine Structure

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    Clock is a signal which synchronizes the logic as well as register read/write activities of a synchronous circuitry. Therefore a good way to design a reliable clock distributor network is always the top priority in IC design. Clock spine is well known for the robustness in clock signal quality delivered. Spine structure had shown good performance in terms of skew, jitter and OCV. Thus this scheme is popular for the high speed circuitry such as CPU chipset design. However, the clock spine is not commonly employed in SoC, due to the design as well as the validation complexity of this scheme. Many SoC design toolsets do not support this scheme up until now. So in this thesis, an automated methodology will be introduced and proven to integrate clock spine into a SoC to distribute a high frequency clock signal. These include the know-how and automation of the methodologies to minimize the complexity of designing the clock spine

    Study on Low-Power Image Processing for Gastrointestinal Endoscopy

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