Biblio

Referring expression comprehension (REF) aims at identifying a particular object in a scene by a natural language expression. It requires joint reasoning over the textual and visual domains to solve the problem. Some popular referring expression datasets, however, fail to provide an ideal test bed for evaluating the reasoning ability of the models, mainly because 1) their expressions typically describe only some simple distinctive properties of the object and 2) their images contain limited distracting information. To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features. First, we design a novel expression engine rendering various reasoning logics that can be flexibly combined with rich visual properties to generate expressions with varying compositionality. Second, to better exploit the full reasoning chain embodied in an expression, we propose a new test setting by adding additional distracting images containing objects sharing similar properties with the referent, thus minimising the success rate of reasoning-free cross-domain alignment. We evaluate several state-of-the-art REF models, but find none of them can achieve promising performance. A proposed modular hard mining strategy performs the best but still leaves substantial room for improvement.

Coupling is a common approach for constructing new chaotic systems. In this paper, I present a novel way of coupling, which is utilized to construct a new chaotic system. Afterwards, the system is analyzed in detail and a pseudorandom bit generator is proposed based on it. Next, I employ five statistic tests to evaluate the pseudo randomness of generated sequences. Linear complexity and cipher space are analyzed at last. All the results demonstrate that the proposed generator possesses excellent properties.