Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.Comment: accepted for ICCV 201

Similarity learning for person re-identification and semantic video retrieval

Many computer vision problems boil down to the learning of a good visual similarity function that calculates a score of how likely two instances share the same semantic concept. In this thesis, we focus on two problems related to similarity learning: Person Re-Identification, and Semantic Video Retrieval. Person Re-Identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. Starting with two cameras, we propose a novel visual word co-occurrence based appearance model to measure the similarities between pedestrian images. This model naturally accounts for spatial similarities and variations caused by pose, illumination and configuration changes across camera views. As a generalization to multiple camera views, we introduce the Group Membership Prediction (GMP) problem. The GMP problem involves predicting whether a collection of instances shares the same semantic property. In this context, we propose a novel probability model and introduce latent view-specific and view-shared random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our method is tested on various benchmarks demonstrating superior accuracy over state-of-art. Semantic Video Retrieval seeks to match complex activities in a surveillance video to user described queries. In surveillance scenarios with noise and clutter usually present, visual uncertainties introduced by error-prone low-level detectors, classifiers and trackers compose a significant part of the semantic gap between user defined queries and the archive video. To bridge the gap, we propose a novel probabilistic activity localization formulation that incorporates learning of object attributes, between-object relationships, and object re-identification without activity-level training data. Our experiments demonstrate that the introduction of similarity learning components effectively compensate for noise and error in previous stages, and result in preferable performance on both aerial and ground surveillance videos. Considering the computational complexity of our similarity learning models, we attempt to develop a way of training complicated models efficiently while remaining good performance. As a proof-of-concept, we propose training deep neural networks for supervised learning of hash codes. With slight changes in the optimization formulation, we could explore the possibilities of incorporating the training framework for Person Re-Identification and related problems.2019-07-09T00:00:00

Symbolic Capital, Existential Insecurity, and Industrial Policies: A Neo-Bourdieusian Theory of the Leninist State in China (1927-1982)

HonorsPolitical ScienceUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/167896/1/yutingch.pd

The Role of China's Social Credit Management in the Socialist Market Economy System

Social credit management is a global issue, has become an important aspect of the economic development of various countries. In the process of social transformation in modern China, the social credit management system has undergone three stages of evolution: from scratch to existence, from existence to reality, and from reality to refinement. Based on the review of the transformation of market order and the construction of social credit system in China, this paper makes a systematic analysis of its main content and internal logic. From the perspective of Huntington's "political decline" and other Chinese and foreign theories, this paper discusses the relationship between the three main bodies of social credit construction - market, society, government and market order respectively, in an attempt to provide inspiration and ideas for the construction of China's future social credit system

Passive detection of moving aerial target based on multiple collaborative GPS satellites

Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance−velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite