Attributes are more and more popular for enhancing the performance of video captioning which requires semantic understanding of videos and the ability of generating natural language descriptions. However, existing methods have flaws in detecting visual attributes. As a result, the captioning model may be misled by those erroneous attributes. Besides, each semantic attribute plays a different role in the next-word generation. How to utilize them effectively in video captioning task is still a challenge. To tackle these problems, in this paper, we propose a novel framework which imposes an attention mechanism guided by the visual attention on the detected video attributes to make a soft-selection over them. Simultaneously, the reinforcement learning algorithm is employed with the motivation to better select the useful attributes. Experimental results on benchmark datasets demonstrate that the proposed attribute selection mechanism can focus on appropriate attributes and boost the caption models.

Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlusion, and appearance deformation. To overcome these difficulties, a reliable point assignment (RPA) algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a Kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the Kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.

The World Wide Web is one of the most popular applications currently running on the Internet, and its size is growing exponentially. Web caching is an important technique that aims to reduce network traffic, server load, and user-perceived retrieval delays by replicating popular contents on proxy servers. Anycast is a new network service widely used for providing auto-configuration and load-balancing. We present an anycast-based cooperative proxy algorithm (ACPA) that brings a server ienearestle to a client to improve overall performance by selective allowing duplicates and migrate objects to other proxies based on request pattern. Analytical and experimental results show that the algorithm outperforms a hash-based algorithm in response time and hop counts.

Ad hoc networks are a new kind of mobile computer network with broad applications and important commercial values. Most proposed on-demand routing protocols for Ad hoc networks build and rely on single path. However, the single path is easily broken and needs to perform a route discovery process again due to the dynamic topology of ad hoc networks. In Aa hoc networks multipath routing is better suited than single path in stability and load balance. Our interests lie in how to get a stable route that can be used for a longer time without rerouting to recover from the path breakage. Our analysis of the multipath's stability shows that the stability of multipath routing is closely related to the routing policies. An independent stability-based routing scheme that is based on DSR is then presented together with the utilization of a group of independent paths for routing. The stable multipath embedded in our muting policies can enhance the performance by decreasing rerouting overheads. The simulation results show that the proposed algorithm can be more adaptive the mobile environment and outperform SMR and DSR.