Real Data Domains Research Articles

Domain-Translated 3D Object Pose Estimation.

Synthetic 3D object models have been proven crucial in object pose estimation, as they are utilized to generate a huge number of accurately annotated data. The object pose estimation problem is usually solved for images originating from the real data domain by employing synthetic images for training data enrichment, without fully exploiting the fact that synthetic and real images may have different data distributions. In this work, we argue that 3D object pose estimation problem is easier to solve for images originating from the synthetic domain, rather than the real data domain. To this end, we propose a 3D object pose estimation framework consisting of a two-step process, where a novel pose-oriented image-to-image translation step is first employed to translate noisy real images to clean synthetic ones and then, a 3D object pose estimation method is applied on the translated synthetic images to finally predict the 3D object poses. A novel pose-oriented objective function is employed for training the image-to-image translation network, which enforces that pose-related object image characteristics are preserved in the translated images. As a result, the pose estimation network does not require real data for training purposes. Experimental evaluation has shown that the proposed framework greatly improves the 3D object pose estimation performance, when compared to state-of-the-art methods.

Multi-pseudo Regularized Label for Generated Data in Person Re-Identification.

Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images (i.e., annotation), the amount of available training data (i.e., real data) is always limited. To produce more data for training a deep network, Generative Adversarial Network (GAN) can be used to generate artificial sample data (i.e., generated data). However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to predefined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets respectively, and outperforms state-of-the-art methods.

Employing finite-state machines in data integrity problems

This paper explores the issue of group integrity of tuple subsets regarding corporate integrity constraints in relational databases. A solution may be found by applying the finite-state machine theory to guarantee group integrity of data. We present a practical guide to coding such an automaton. After creating SQL queries to manipulate data and control its integrity for real data domains, we study the issue of query performance, determine the level of transaction isolation, and generate query plans.