Differentiable SAR Renderer and SAR Target Reconstruction

Forward modeling of wave scattering and radar imaging mechanisms is the key to information extraction from synthetic aperture radar (SAR) images. Like inverse graphics in optical domain, an inherently-integrated forward-inverse approach would be promising for SAR advanced information retrieval and target reconstruction. This paper presents such an attempt to the inverse graphics for SAR imagery. A differentiable SAR renderer (DSR) is developed which reformulates the mapping and projection algorithm of SAR imaging mechanism in the differentiable form of probability maps. First-order gradients of the proposed DSR are then analytically derived which can be back-propagated from rendered image/silhouette to the target geometry and scattering attributes. A 3D inverse target reconstruction algorithm from SAR images is devised. Several simulation and reconstruction experiments are conducted, including targets with and without background, using both synthesized data or real measured inverse SAR (ISAR) data by ground radar. Results demonstrate the efficacy of the proposed DSR and its inverse approach

Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted strategies of sliding window over the entire video or exhaustively ranking all possible clip-sentence pairs in a pre-segmented video, which inevitably suffer from exhaustively enumerated candidates. To alleviate this problem, we formulate this task as a problem of sequential decision making by learning an agent which regulates the temporal grounding boundaries progressively based on its policy. Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training. Our proposed framework achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset and Charades-STA dataset while observing only 10 or less clips per video.Comment: AAAI 201

Coordinating a Supply Chain When Manufacturer Makes Cost Reduction Investment in Supplier

We consider a supply chain consisting of an upstream supplier and a downstream manufacturer, in which the supplier provides a component to the manufacturer, facing a price-sensitive and uncertain demand. The manufacturer makes cost reduction investment in the supplier to improve the supplier’s production efficiency, which benefits the entire supply chain. We derive the optimal investment and operating decisions. Both the centralized and decentralized supply chains are studied. We show that the optimal investment and operating decisions in the decentralized setting may deviate from that in the centralized setting. To avoid the profit loss caused by such a deviation, we develop a coordination mechanism by introducing a combined policy of revenue-sharing policy and investment cost-sharing policy. We also show that the developed coordination mechanism can achieve Pareto improvement for the two players