Narrative videos usually illustrate the main content through multiple narrative information such as audios, video frames and subtitles. Existing video summarization approaches rarely consider the multiple dimensional narrative inputs, or ignore the impact of shots artistic assembly when directly applied to narrative videos. This paper introduces a multimodal-based and aesthetic-guided narrative video summarization method. Our method leverages multimodal information including visual content, subtitles and audio information through our specified key shots selection, subtitle summarization, and highlight extraction components. Furthermore, under the guidance of cinematographic aesthetic, we design a novel shots assembly module to ensure the shot content completeness and then assemble the selected shots into a desired summary. Besides, our method also provides the flexible specification for shots selection, to achieve which it automatically selects semantically related shots according to the user-designed text. By conducting a large number of quantitative experimental evaluations and user studies, we demonstrate that our method effectively preserves important narrative information of the original video, and it is capable of rapidly producing high-quality and aesthetic-guided narrative video summaries.

Video summarization aims to generate a compact version of a lengthy video that retains its primary content. In general, humans are gifted with producing a high-quality video summary, because they acquire crucial content through multiple dimensional information and own abundant background knowledge about the original video. However, existing methods rarely consider multichannel information and ignore the impact of external knowledge, resulting in the limited quality of the generated summaries. This paper proposes a knowledge augmented and multimodal-based video summarization method, termed KAMV, to address the problem above. Specifically, we design a knowledge encoder with a hybrid method consisting of generation and retrieval, to capture descriptive content and latent connections between events and entities based on the external knowledge base, which can provide rich implicit knowledge for better comprehending the video viewed. Furthermore, for the sake of exploring the interactions among visual, audio, implicit knowledge and emphasizing the content that is most relevant to the desired summary, we present a fusion module under the supervision of these multimodal information. By conducting extensive experiments on four public datasets, the results demonstrate the superior performance yielded by the proposed KAMV compared to the state-of-the-art video summarization approaches.

Existing color editing algorithms enable users to edit the colors in an image according to their own aesthetics. Unlike artists who have an accurate grasp of color, ordinary users are inexperienced in color selection and matching, and allowing non-professional users to edit colors arbitrarily may lead to unrealistic editing results. To address this issue, we introduce a palette-based approach for realistic object-level image recoloring. Our data-driven approach consists of an offline learning part that learns the color distributions for different objects in the real world, and an online recoloring part that first recognizes the object category, and then recommends appropriate realistic candidate colors learned in the offline step for that category. We also provide an intuitive user interface for efficient color manipulation. After color selection, image matting is performed to ensure smoothness of the object boundary. Comprehensive evaluation on various color editing examples demonstrates that our approach outperforms existing state-of-the-art color editing algorithms.

Clustering coefficient plays an important role in many real-world applications, such as social network analysis and community mining. However, it is expensive to compute exact clustering coefficient for large networks. In many real-world applications, estimating clustering coefficient via random walk method is preferable because it is efficient and accurate. MapReduce is a popular distributed programming framework for processing large datasets. In this paper, we propose an algorithm on MapReduce framework to estimate clustering coefficient based on random walk method. Experiments on a Hadoop cluster for large real-world graphs demonstrate that the proposed algorithm is accurate and efficient. Comparing to Doubling algorithm, a state-of-the-art distributed algorithm to implement random walk on MapReduce, the proposed algorithm runs much faster. The proposed algorithm also reduces I/O cost efficiently.

With the growing power of GPUs, how to utilize the high computing performance provided by the GPU hardware becomes an urgent yet challenging problem, especially for applications with fine grained parallelism. Task programming is efficient for handling fine grained parallelism but current GPU task parallel solutions using either concurrent kernel execution (CKE) or persistent kernels suffer from a high cost of CPU-GPU interaction. The page-locked host memory supported by new generation GPUs turns CPU-GPU heterogeneous systems into the non-uniform memory access (NUMA) architecture, making it possible to improve CPU-GPU interaction with shared memory programming. In this paper, we propose the CPU-assisted GPU thread pool (CAGTP) model that combines data parallelism and task parallelism at the thread block level to support applications with fine grained parallelism. In the CAGTP model, the Computing Block Level task Scheduling (CBLS) method is designed in which task slots allocated in the page-locked host memory eliminate competition among thread blocks. A separate host scheduler is designed for scheduling tasks to thread blocks and the overhead for scheduling a task (200ns) is much lower than that of similar systems. Experiment results show that the CAGTP model supports fine grained task parallelism with or without dependencies efficiently. It outperforms CKE for batched GEMMs, Cholesky factorization and mixed workloads.

Network-on-a-chip (NoC) is an effective approach to connect and manage the communication between the variety of design elements and intellectual property blocks required in large and complex system-on-chips. In this paper, we propose a new NoC architecture, referred as the Quartered Recursive Diagonal Torus (QRDT), which is constructed by overlaying diagonal torus. Due to its small diameter and rich routing recourses, QRDT is determined to be well suitable to construct highly scalable NoCs.

Recursive Diagonal Torus (RDT), a class of interconnection network is proposed for massively parallel computers with up to 2/sup 16/ nodes. By making the best use of a recursively structured diagonal mesh (torus) connection, the RDT has a smaller diameter (e.g., it is 11 for 2/sup 10/ nodes) with a smaller number of links per node (i.e., 8 links per node) than those of the hypercube. A simple routing algorithm, called vector routing, which is near-optimal and easy to implement is also proposed. Although the congestion on upper rank tori sometimes degrades the performance under the random traffic, the RDT provides much better performance than that of a 2D/3D torus in most cases and, under hot spot traffic, the RDT provides much better performance than that of a 2D/3D/4D torus. The RDT router chip which provides a message multicast for maintaining cache consistency is available. Using the 0.5 /spl mu/m BICMOS SOG technology, versatile functions, including hierarchical multicasting, combining acknowledge packets, shooting down/restart mechanism, and time-out/setup mechanisms, work at a 60 MHz clock rate.