Recurrent Attentional Networks for Saliency Detection

Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent network units, RACDNN is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively. Besides tackling the scale problem, RACDNN can also learn context-aware features from past iterations to enhance saliency refinement in future iterations. Experiments on several challenging saliency detection datasets validate the effectiveness of RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection methods.Comment: CVPR 201

SANet: Structure-Aware Network for Visual Tracking

Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as a classification problem. However, these trackers are sensitive to similar distractors because their CNN models mainly focus on inter-class classification. To address this problem, we use self-structure information of object to distinguish it from distractors. Specifically, we utilize recurrent neural network (RNN) to model object structure, and incorporate it into CNN to improve its robustness to similar distractors. Considering that convolutional layers in different levels characterize the object from different perspectives, we use multiple RNNs to model object structure in different levels respectively. Extensive experiments on three benchmarks, OTB100, TC-128 and VOT2015, show that the proposed algorithm outperforms other methods. Code is released at http://www.dabi.temple.edu/~hbling/code/SANet/SANet.html.Comment: In CVPR Deep Vision Workshop, 201

This Time It's Personal: from PIM to the Perfect Digital Assistant

Interacting with digital PIM tools like calendars, to-do lists, address books, bookmarks and so on, is a highly manual, often repetitive and frequently tedious process. Despite increases in memory and processor power over the past two decades of personal computing, not much has changed in the way we engage with such applications. We must still manually decompose frequently performed tasks into multiple smaller, data specific processes if we want to be able to recall or reuse the information in some meaningful way. "Meeting with Yves at 5 in Stata about blah" breaks down into rigid, fixed semantics in separate applications: data to be recorded in calendar fields, address book fields and, as for the blah, something that does not necessarily exist as a PIM application data structure. We argue that a reason Personal Information Management tools may be so manual, and so effectively fragmented, is that they are not personal enough. If our information systems were more personal, that is, if they knew in a manner similar to the way a personal assistant would know us and support us, then our tools would be more helpful: an assistive PIM tool would gather together the necessary material in support of our meeting with Yves. We, therefore, have been investigating the possible paths towards PIM tools as tools that work for us, rather than tools that seemingly make us work for them. To that end, in the following sections we consider how we may develop a framework for PIM tools as "perfect digital assistants" (PDA). Our impetus has been to explore how, by considering the affordances of a Real World personal assistant, we can conceptualize a design framework, and from there a development program for a digital simulacrum of such an assistant that is not for some far off future, but for the much nearer term

Overview of contextual tracking approaches in information fusion

Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad