In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks. Inspired by recent bounding box regression methods for object detection, we study the regression capability of Long Short-Term Memory (LSTM) in the temporal domain, and propose to concatenate high-level visual features produced by convolutional networks with region information. In contrast to existing deep learning based trackers that use binary classification for region candidates, we use regression for direct prediction of the tracking locations both at the convolutional layer and at the recurrent unit. Our extensive experimental results and performance comparison with state-of-the-art tracking methods on challenging benchmark video tracking datasets shows that our tracker is more accurate and robust while maintaining low computational cost. For most test video sequences, our method achieves the best tracking performance, often outperforms the second best by a large margin.
No abstract
Most existing Quality of Experience (QoE)-driven multimedia resource allocation methods assume that the QoE model of each user is known to the controller before the start of the multimedia playout. However, this assumption may be invalid in many practical scenarios. In this paper, we address the resource allocation problem with incomplete information where the realized mean opinion score (MOS) can only be observed over time, but the underlying QoE model and playout time are unknown. We consider two variants of this problem: 1) the form of the QoE model is known but the parameters are unknown; 2) both the form and the parameters of the QoE model are unknown. For both cases, we develop dynamic resource allocation schemes based on online test-optimization strategy. Simply speaking, one first spends appropriate time on testing the QoE model, then optimizes the sum of the MOS in the remaining playout time. The highlight of this paper lies in resolving the inherent tension between the test and optimization by jointly considering the uncertainties of QoE model and playout time. Furthermore, we derive tight bounds on the MOS loss incurred by the proposed schemes in comparison with the optimal scheme that knows the QoE model a priori and prove that the performance gap, as the playout time tends to infinity, asymptotically shrinks to zero.
Smartphones and tablets with their apps pervaded our everyday life, leading to a new demand for search tools to help users find the right apps to satisfy their immediate needs. While there are a few commercial mobile app search engines available, the new task of mobile app retrieval has not yet been rigorously studied. Indeed, there does not yet exist a test collection for quantitatively evaluating this new retrieval task. In this paper, we first study the effectiveness of the state-of-the-art retrieval models for the app retrieval task using a new app retrieval test data we created. We then propose and study a novel approach that generates a new representation for each app. Our key idea is to leverage user reviews to find out important features of apps and bridge vocabulary gap between app developers and users. Specifically, we jointly model app descriptions and user reviews using topic model in order to generate app representations while excluding noise in reviews. Experiment results indicate that the proposed approach is effective and outperforms the state-of-the-art retrieval models for app retrieval.
<p class="MsoNormal" style="margin: 0cm 0cm 0pt; layout-grid-mode: char;"><span style="font-family: Times New Roman; font-size: x-small;">With the fast-paced development of computing technologies, mobile devices have almost sufficient computation and communication capabilities to support mobile multimedia applications such as multimedia streaming, VoIP, and mobile TV. However, most existing mobile devices are powered by battery with limited energy resource. To support multimedia on battery-powered mobile devices, how to efficiently utilize the limited power source and other limited network resources has become one of the major challenges in mobile multimedia system design. It has been shown that achieving a satisfactory user experience needs a systematic consideration of both video source adaptation and network transmission adaptation, indicating that the core of mobile multimedia system design is how to achieve a good balance of the power consumption between computation usage and communication usage. In other words, it depends on how to jointly select video source parameters and channel parameters based on the video content characteristics, available network resources, and underlying network conditions, given the fact that the power management schemes as well as the nonlinear battery effects also affect the system power consumption of mobile devices. In this paper, we review the recent advances in power-aware mobile multimedia, especially the adaptation technologies applied in video coding and delivery. In addition, the major research challenges in the field are demonstrated and discussed, which include power-management for mobile devices, rate-distortion-complexity optimized video codec design, and computational complexity and power aware cross-layer design and optimization. At the end, we propose a number of future research directions for audiences to continue investigation in this field. </span></p>
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