Relation Networks for Object Detection

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector

Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released

Effective Bug Triage based on Historical Bug-Fix Information

International audienceFor complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this pa-per, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called BugFixer, which recommends developers for a new bug report based on historical bug-fix in-formation. BugFixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship be-tween the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, BugFixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate BugFixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects

Quantum steering for two-mode states with Continuous-variable in laser channel

The Einstein-Podolsky-Rosen steering is an important resource for one-sided device independent quantum information processing. This steering property will be destroyed during the interaction between quantum system and environment for some practical applications. In this paper, we use the representation of characteristic function for probability to examine the quantum steering of two-mode states with continuous-variable in laser channel, where both the gain factor and the loss effect are considered. Firstly, we analyse the steering time of two-mode squeezed vacuum state under one-mode and two-mode laser channel respectively. We find the gain process will introduce additional noise to the two-mode squeezed vacuum state such that the steerable time is reduced. Secondly, by quantising quantum Einstein-Podolsky-Rosen steering, it shows that two-side loss presents a smaller steerability than one-side loss although they share the same two-way steerable time. In addition, we find the more gained party can steer the others state, while the other party cannot steer the gained party in a certain threshold value. In this sense, it seems that the gain effect in one party is equivalent to the loss effect in the others party. Our results pave way for the distillation of Einstein-Podolsky-Rosen steering and the quantum information processing in practical quantum channels