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

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

Distributed optimization with inexact oracle

summary:In this paper, we study the distributed optimization problem using approximate first-order information. We suppose the agent can repeatedly call an inexact first-order oracle of each individual objective function and exchange information with its time-varying neighbors. We revisit the distributed subgradient method in this circumstance and show its suboptimality under square summable but not summable step sizes. We also present several conditions on the inexactness of the local oracles to ensure an exact convergence of the iterative sequences towards the global optimal solution. A numerical example is given to verify the efficiency of our algorithm

Application Of A Polynomial Affine Method In Dynamic Portfolio Choice

This thesis develops numerical approaches to attain optimal multi-period portfolio strategies in the context of advanced stochastic models within expected utility and mean-variance theories. Unlike common buy-and-hold portfolio strategies, dynamic asset allocation reflects the investment philosophy of a portfolio manager that benefits from the most recent market conditions to rebalance the portfolio accordingly. This enables managers to capture fleeting opportunities in the markets thereby enhancing the portfolio performance. However, the solvability of the dynamic asset allocation problem is often non-analytical, especially when considering a high-dimensional portfolio with advanced models mimicking practical asset\u27s return. To overcome this issue, this thesis presents a competitive methodology to approximate optimal dynamic portfolio strategies. The thesis can be categorized into two large sections. The development, algorithmic description, testing and extension of the methodology are presented in detail in the first section. Specifically, the main method, named PAMC, is originally developed for constant relative risk aversion investors. In a comparison with two existing well-known benchmark methods, our approach demonstrates superior efficiency and accuracy, this is not only for cases with no known solution but also for models where the analytical solution is available. We consequently extend the method into the wider hyperbolic absolute risk aversion utility family which is more flexible in capturing the risk aversion of investors. This extension permits the applicability of our method to both expected utility theory and mean-variance theory. Furthermore, the quality of portfolio allocation is directly linked to the quality of the portfolio value function approximation. This generates another important extension: the replacement of the polynomial regression in the original method by neural networks. Besides, we successfully implement the method on two important but closed-form unsolvable models: the Ornstein-Uhlenbeck 4/2 model and the Heston model with a stochastic interest rate, which further confirms the practicality and effectiveness of our novel methodology. The second part of the thesis addresses the application of our numerical method to investments involving financial derivatives. In addition to portfolio performance maximization and given the infinitely many choices of derivatives, we propose another criterion, namely, risk exposure minimization, to help investors meet regulatory constraints and protect their capital in the case of a market crash. The complexity of derivatives’ price dynamics leads to new challenges on the solvability of the optimal allocation for a derivative-based portfolio. With proper modifications, our method is applicable to this type of problem. We then consider a portfolio construction with equity options and volatility index (VIX) options in the presence of volatility risk, providing insight into best investment practices with derivatives

A Practical Algorithm for Topic Modeling with Provable Guarantees

Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood objective. Efficient algorithms exist that approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper we present an algorithm for topic model inference that is both provable and practical. The algorithm produces results comparable to the best MCMC implementations while running orders of magnitude faster.Comment: 26 page

Does Oline Video-Sharing Advertising Have Diffusion Gene?

Video-sharing is one of the most popular applications on the Internet, the development of which subverts the traditional information diffusion path. Online video-sharing advertising emerges quickly at the same time. Quick online video sharing and diffusing of advertising depend heavily on its presentation of entertainment content and its display format. This article classifies the entertainment content of online video-sharing advertising (VSA) into humor and funny content (HFC), focus event content (FEC), and sex and nudity content (SNC); and presents the display format of online VSA into real format and anthropomorphic format. Hence, this article has conducted a research on the possible relationship between these two factors and how they influence the effects of online video-sharing advertising. This experimental study confirms that entertainment content and display format are the most critical factors to audiences in sharing and diffusing the online VSA. It also finds out that if advertisers use HFC as the entertainment content of online VSA, the best display format of online VSA is the realistic format; and if advertisers use SNC as the entertainment content of online VSA, the best display format of online VSA is the anthropomorphic format

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape