Conclusion-Supplement Answer Generation for Non-Factoid Questions

This paper tackles the goal of conclusion-supplement answer generation for non-factoid questions, which is a critical issue in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI), as users often require supplementary information before accepting a conclusion. The current encoder-decoder framework, however, has difficulty generating such answers, since it may become confused when it tries to learn several different long answers to the same non-factoid question. Our solution, called an ensemble network, goes beyond single short sentences and fuses logically connected conclusion statements and supplementary statements. It extracts the context from the conclusion decoder's output sequence and uses it to create supplementary decoder states on the basis of an attention mechanism. It also assesses the closeness of the question encoder's output sequence and the separate outputs of the conclusion and supplement decoders as well as their combination. As a result, it generates answers that match the questions and have natural-sounding supplementary sequences in line with the context expressed by the conclusion sequence. Evaluations conducted on datasets including "Love Advice" and "Arts & Humanities" categories indicate that our model outputs much more accurate results than the tested baseline models do.Comment: AAAI-2020 (Accepted

Improved depth map generation for efficient transmission of multi-lens stereoscopic video (translation of the Japanese title)

info:eu-repo/semantics/publishe

Synthesis Error Compensated Multiview Video Plus Depth For Representation of Multiview Video

info:eu-repo/semantics/publishe

Subjective Evaluation of Compression Performance of Synthesis Error Compensated Multiview Video plus Depth

info:eu-repo/semantics/publishe

Implementing and Evaluation of SECOND-MVD Method to Multiview Video Transmission System REI

info:eu-repo/semantics/publishe

Robust and Automatic Calibration of a Projection See-Through Integral Imaging Display using Homography Based Mirror Identification

info:eu-repo/semantics/publishe

Homography based identification for automatic and robust calibration of projection integral imaging displays

Recent advances in the creation of microlens arrays as holographic optical elements allow the creation of projector-based see-through light field displays suitable for augmented reality. These systems require an accurate calibration of the projector with relation to the microlens array, as any small misalignment causes the 3D reconstruction to fail. The methods reported so far require precise placement of the calibration camera w.r.t. the lens array screen, which affects the display configuration. We propose a calibration approach which is more robust, and which allows free camera placement. Hence, it does not limit the capabilities of the system. Both a homography-based technique and structured light play a central role in realizing such a method. The method was tested on a projection-based integral imaging display system consisting of a consumer-grade projector and a digitally designed holographic optical element based micromirror array screen. The calibration method compensates for the lens distortion, intrinsics, and positioning of the projector with relation to the screen. The method uses a single camera and does not require the use of obtrusive markers as reference. We give an in-depth explanation of the different steps of the algorithm, and verify the calibration using both a simulated and a real-world setup.SCOPUS: ar.jinfo:eu-repo/semantics/publishe