TVPR: Text-to-Video Person Retrieval and a New Benchmark

Most existing methods for text-based person retrieval focus on text-to-image person retrieval. Nevertheless, due to the lack of dynamic information provided by isolated frames, the performance is hampered when the person is obscured in isolated frames or variable motion details are given in the textual description. In this paper, we propose a new task called Text-to-Video Person Retrieval(TVPR) which aims to effectively overcome the limitations of isolated frames. Since there is no dataset or benchmark that describes person videos with natural language, we construct a large-scale cross-modal person video dataset containing detailed natural language annotations, such as person's appearance, actions and interactions with environment, etc., termed as Text-to-Video Person Re-identification (TVPReid) dataset, which will be publicly available. To this end, a Text-to-Video Person Retrieval Network (TVPRN) is proposed. Specifically, TVPRN acquires video representations by fusing visual and motion representations of person videos, which can deal with temporal occlusion and the absence of variable motion details in isolated frames. Meanwhile, we employ the pre-trained BERT to obtain caption representations and the relationship between caption and video representations to reveal the most relevant person videos. To evaluate the effectiveness of the proposed TVPRN, extensive experiments have been conducted on TVPReid dataset. To the best of our knowledge, TVPRN is the first successful attempt to use video for text-based person retrieval task and has achieved state-of-the-art performance on TVPReid dataset. The TVPReid dataset will be publicly available to benefit future research.

Resource-Driven Mission-Phasing Techniques for Constrained Agents in Stochastic Environments

Because an agents resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of the environment. Such agents can, in general, achieve more of their objectives if they can use --- and even create --- opportunities to change which resources they hold at various times. Driven by resource constraints, the agents could break their overall missions into an optimal series of phases, optimally reconfiguring their resources at each phase, and optimally using their assigned resources in each phase, given their knowledge of the stochastic environment. In this paper, we formally define and analyze this constrained, sequential optimization problem in both the single-agent and multi-agent contexts. We present a family of mixed integer linear programming (MILP) formulations of this problem that can optimally create phases (when phases are not predefined) accounting for costs and limitations in phase creation. Because our formulations multaneously also find the optimal allocations of resources at each phase and the optimal policies for using the allocated resources at each phase, they exploit structure across these coupled problems. This allows them to find solutions significantly faster(orders of magnitude faster in larger problems) than alternative solution techniques, as we demonstrate empirically.