With the blooming of online video applications, live commenting is an emerging feature of online video sites. The live video comments generation (LVCG) task aims to generate live comments for videos while considering both the video and the surrounding comments made by other viewers. In this work, we aim to improve the relevance between live comments and videos by modeling the cross-modal interactions among different modalities. To overcome the problem of insufficient multimodal interactions for live video comments generation, we built two basic attention blocks: the self attention (SA) block that can model the dense intramodal interactions; and the x-guided attention (XGA) block to model the dense intermodal interactions. After that, by modular compositions of the SA and XGA blocks, we propose different multimodal transformer architectures to handle the multimodal features. Finally, experiments show that our proposed multimodal guided attention models significantly outperform previous methods in most of the metrics.
Recently, terahertz (THz) computed tomography (CT) has emerged as a possible effective technique for 3D structural information detection. However, THz-CT is difficult to be applied to high refractive index object, due to the severe refraction phenomenon occurred during the acquisition of raw data. We propose a novel experimental procedure to solve this problem. Including the use of a sink filled with liquid whose refractive index is close to the sample, and a correction algorithm to eliminate the noise of liquid. The proposed method is applied to the high-density polyethylene samples of different shapes.
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