Tencent AVS: A Holistic Ads Video Dataset for Multi-modal Scene Segmentation

Temporal video segmentation and classification have been advanced greatly by public benchmarks in recent years. However, such research still mainly focuses on human actions, failing to describe videos in a holistic view. In addition, previous research tends to pay much attention to visual information yet ignores the multi-modal nature of videos. To fill this gap, we construct the Tencent `Ads Video Segmentation'~(TAVS) dataset in the ads domain to escalate multi-modal video analysis to a new level. TAVS describes videos from three independent perspectives as `presentation form', `place', and `style', and contains rich multi-modal information such as video, audio, and text. TAVS is organized hierarchically in semantic aspects for comprehensive temporal video segmentation with three levels of categories for multi-label classification, e.g., `place' - `working place' - `office'. Therefore, TAVS is distinguished from previous temporal segmentation datasets due to its multi-modal information, holistic view of categories, and hierarchical granularities. It includes 12,000 videos, 82 classes, 33,900 segments, 121,100 shots, and 168,500 labels. Accompanied with TAVS, we also present a strong multi-modal video segmentation baseline coupled with multi-label class prediction. Extensive experiments are conducted to evaluate our proposed method as well as existing representative methods to reveal key challenges of our dataset TAVS

A digital image analysis of gravel aggregate using CT scanning technique

Particle shape was one of the most important factors which affects the gravel aggregate’s properties. It was also one of the important factors that directly affects the performance of asphalt pavements. In this paper, the gravel aggregate of quartzite was studied by using the industrial CT instrument. MATLAB was used to capture the aggregate slice properties including reverse color, median filtering, noise reduction, binarization and so on. The 3D aggregate model was reconstructed by using the software of MIMICS. The three-dimensional model of the aggregate was further optimized. The best fitting cuboid, cylinder, cone and sphere information of the aggregate were obtained by using the characteristics analysis function. Keywords: CT scanning, 3D reconstruction, Watershed transform, Particle shape, Gravel aggregat