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Point cloud segmentation is essential for scene understanding, which provides advanced information for many applications, such as autonomous driving, robots, and virtual reality. To improve the accuracy and robustness of point cloud segmentation, many researchers have attempted to fuze camera images to complement the color and texture information. The common fusion strategy is the combination of convolutional operations with concatenation, element-wise addition or element-wise multiplication. However, conventional convolutional operators tend to confine the fusion of modal features within their receptive fields, which can be incomplete and limited. In addition, the inability of encoder–decoder segmentation networks to explicitly perceive segmentation boundary information results in semantic ambiguity and classification errors at object edges. These errors are further amplified in point cloud segmentation tasks, significantly affecting the accuracy of point cloud segmentation. To address the above issues, we propose a novel self-attention multi-modal fusion semantic segmentation network for point cloud semantic segmentation. Firstly, to effectively fuze different modal features, we propose a self-cross fusion module (SCF), which models long-range modality dependencies and transfers complementary image information to the point cloud to fully leverage the modality-specific advantages. Secondly, we design the salience refinement module (SR), which calculates the importance of channels in the feature maps and global descriptors to enhance the representation capability of salient modal features. Finally, we propose the local-aware anisotropy loss measure the element-level importance in the data and explicitly provide boundary information for the model, which alleviates the inherent semantic ambiguity problem in segmentation networks. Extensive experiments on two benchmark datasets demonstrate that our proposed method surpasses current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

ACM Transactions on Multimedia Computing, Communications, and Applications has been dedicated to advancing multimedia research, fostering discoveries, innovations, and practical applications since 2005. The journal consistently publishes top-notch, original research in emerging fields through open submissions, calls for articles, special issues, rigorous review processes, and diverse research topics. This study aims to delve into an extensive bibliometric analysis of the journal, utilising various bibliometric indicators. The article seeks to unveil the latent implications within the journal's scholarly landscape from 2005 to 2022. The data primarily draws from the Web of Science Core Collection database. The analysis encompasses diverse viewpoints, including yearly publication rates and citations, identifying highly cited articles, and assessing the most prolific authors, institutions, and countries. The article employs VOSviewer-generated graphical maps, effectively illustrating networks of co-citations, keyword co-occurrences, and institutional and national bibliographic couplings. Furthermore, the study conducts a comprehensive global and temporal examination of co-occurrences of the author's keywords. This investigation reveals the emergence of numerous novel keywords over the past decades. [ABSTRACT FROM AUTHOR]

Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle these issues in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on extra equipment in the conference venues, such as the infrared camera or special microphone, which are not practical. In this article, we design and implement Meetor, a human-centered automatic video editing system for meeting recordings. The Meetor mainly contains three parts: an audio-based video synchronization algorithm, human-centered video content flaw detection algorithms, and an automatic video editing algorithm. Two main experiments are conducted from both objective and subjective aspects to evaluate the performance of the Meetor. The experimental results on a testbed illustrate that the proposed algorithms could achieve state-of-the-art (SOTA) performance in video content flaw detection. However, the conducted user study demonstrates that Meetor could generate meeting recordings with a satisfactory quality compared with professional video editors. Moreover, we also present a practical application of the Meetor in a university campus prototype, in which the Meetor is applied in the automatic editing of lecture recordings. All in all, the proposed Meetor can be utilized in practical applications to release the workload of professional video editors. [ABSTRACT FROM AUTHOR]