Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification

Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to learn identity information from labeled images in source domains and apply it to unlabeled images in a target domain. One major issue with many unsupervised re-identification methods is that they do not perform well relative to large domain variations such as illumination, viewpoint, and occlusions. In this paper, we propose a Synthesis Model Bank (SMB) to deal with illumination variation in unsupervised person re-ID. The proposed SMB consists of several convolutional neural networks (CNN) for feature extraction and Mahalanobis matrices for distance metrics. They are trained using synthetic data with different illumination conditions such that their synergistic effect makes the SMB robust against illumination variation. To better quantify the illumination intensity and improve the quality of synthetic images, we introduce a new 3D virtual-human dataset for GAN-based image synthesis. From our experiments, the proposed SMB outperforms other synthesis methods on several re-ID benchmarks.Comment: 10 pages, 5 figures, 5 table

Transmission of multiple description and layered video over an EGPRS wireless network

We investigate the ability of multiple descriptions (MD) and layered coding to improve the quality of video transmitted over EGPRS networks. One-layer video sent over a single channel on such a network has a fairly sharp quality transition, depending on a user’s location. Either the video can be transmitted reliably (if the video rate is less than or equal to what the channel can sustain), or it is subjected to many lost packets. In this system, MD and layered video may offer two ways to improve video quality beyond that of the one-layer video. First, each sub-stream can be sent on a separate channel, essentially doubling the assigned bandwidth and increasing video quality. Second, MD and layered video are more error resilient than one-layer video, potentially improving the video quality seen by users in poor locations. We find that for the system scenarios considered, one- and two-layer coding outperform MD coding, depending upon the number of wireless channels used for the video transport. 1

Discrete-time rewards model-checked

This paper presents a model-checking approach for analyzing discrete-time Markov reward models. For this purpose, the temporal logic probabilistic CTL is extended with reward constraints. This allows to formulate complex measures – involving expected as well as accumulated rewards – in a precise and succinct way. Algorithms to efficiently analyze such formulae are introduced. The approach is illustrated by model-checking a probabilistic cost model of the IPv4 zeroconf protocol for distributed address assignment in ad-hoc networks