Towards High Performance Video Object Detection

There has been significant progresses for image object detection in recent years. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Built upon the recent works, this work proposes a unified approach based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Our approach extends prior works with three new techniques and steadily pushes forward the performance envelope (speed-accuracy tradeoff), towards high performance video object detection

Emerging Superconductivity and Topological States in Bismuth Chalcogenides

In this chapter, we review the recent experimental work in emerging superconductors, i.e., bismuth chalcogenides, including the newly discovered BiS(e)2-based layered superconductors and some topological superconductor candidates. Their crystal structure and various physical properties are reviewed in detail, with the correlation between structure and superconductivity as the main clue throughout this chapter. Bi2OS2 is the simplest structure in Bi─O─S compounds and probably the parent compound of this series. Superconductivity emerges when carriers are introduced by intercalation or chemical substitution. The superconducting layer is extended to BiSe2 layer in LaO1−xFxBiSe2, which has an improved superconductivity. Moreover, the topological insulator Bi2Se3 can be turned into superconductors by intercalating metal atoms into van der Waals space, e.g., SrxBi2Se3, a potential topological superconductor, whose quantum oscillations reveal a possible topological surface state. The intermediate external pressure can efficiently suppress superconductivity, which reemerges when pressure is further increased, while Tc is nearly invariant in high-pressure region, indicating an unconventional pairing state

Flow-Guided Feature Aggregation for Video Object Detection

Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The code is available at https://github.com/msracver/Flow-Guided-Feature-Aggregation

A Photovoltaic Solar Tracking System with Bidirectional Sliding Axle for Building Integration

AbstractA new single-axis solar tracking device is designed and explored, which is able to lift and lower the photovoltaic panels. The photovoltaic panels can be tilted to east-west directions in the process of tracking the sun. In windy weather, the solar panels can be placed close to horizontal rail by using stent, which can minimize the frontal area. What's more, the mechanical strength of this device is better than traditional single-axis solar tracking system, so as to enhance wind resistance in windy weather. The device in this paper is suitable for PV power plants on building roofs because it can meet the strict requirements of wind resistance capacity and safety