# Dynamic Scene Segmentation and Sentiment Analysis for Danmaku
## 2 Related Works
### Scene Segmentation Technology  
Scene segmentation is crucial when studying video content. It includes identifying visual boundaries and analyzing and encoding the emotions within the content.  
[PERSON] et al. [3] put forward a scene segmentation method that starts with local details and then moves to a global view. They aimed to address the challenging task of movie scene segmentation. Movie scenes contain more complex temporal structures and semantic information compared to videos in traditional vision media. To achieve their goal, [PERSON] et al. created a large-scale video dataset called MovieScenes. This dataset has 21,000 annotated scene segments from 150 movies. Their proposed local-to-global scene segmentation framework integrates multi-modal information at three levels: clip, segment, and movie. First, it focuses on local details by analyzing clip-level information to capture subtle changes in scenes. Then, it gradually takes into account segment- and movie-level information to consider the overall structure and semantics. This method simplifies the complex meanings in long movies' temporal structures. Experiments on the MovieScenes dataset show that their network can segment movies into scenes with high accuracy, outperforming previous methods. Also, pre-training on MovieScenes can significantly improve the performance of existing methods, promoting the development of scene segmentation technology in handling large-scale and complex video data.  
[PERSON] et al. [4] proposed the DRANet (Dual Relation-Aware Attention Network) for scene segmentation. In pixel-level recognition, effectively using context information is very important. They used the relation-aware attention mechanism to adaptively capture context information. Specifically, they added two types of attention modules on top of the dilated FCN (Fully Convolutional Network). One module models the contextual dependencies in the spatial dimension, and the other does the same in the channel dimension. In these attention modules, they used the self-attention mechanism. This allows each pixel or channel to gather context from all other pixels or channels based on their correlations. To reduce the high cost of calculation and memory caused by the pairwise association calculation, they designed two compact attention modules. In these modules, each pixel or channel only forms associations with a few gathering centers and obtains context aggregation from these centers. At the same time, they added a cross-level gating decoder. This decoder can selectively enhance spatial details, which improves the network's performance. They conducted many experiments on four challenging scene segmentation datasets: Cityscapes, ADE20K, PASCAL Context, and COCO Stuff. The results show that their network is effective and achieves a new state-of-the-art segmentation performance. For example, on the Cityscapes test set, without using extra coarse-annotated data, it achieves a mean IoU score of 82.9%, providing a more effective solution for scene segmentation on complex datasets.  
[PERSON] and [PERSON] [2] presented TransNet V2, a deep network design for the rapid detection of shot transitions. It improved the accuracy of shot transition detection in complex video content, marking significant progress in the initial step of video analysis--shot transition detection--and providing a more reliable basis for subsequent video content analysis.