Deep Learning for Free-Hand Sketch: A Survey <s> Multi-Modal <s> The usefulness of a video database relies on whether the video of interest can be easily located. To allow exploring, browsing, and retrieving videos according to their visual content, efficient techniques for evaluating the visual similarity between different video clips are necessary. We present a framework for measuring video similarity across different resolutions-both spatial and temporal. In particular, the video clips to be compared can be properly aligned through the use of suitable weighting functions and alignment constraints. Dynamic programming techniques are employed to obtain the video similarity measure with a reasonable computational cost. An application to searching MPEG compressed video by example is presented to demonstrate the potential use of the proposed video similarity measure. <s> BIB001 </s> Deep Learning for Free-Hand Sketch: A Survey <s> Multi-Modal <s> We propose a sketch-based system for manga image retrieval. In the system, users simply draw sketches, and similar images are retrieved in real time from a manga database. The results are updated every time the user draws a stroke and therefore users can intuitively interact with the system. The proposed method consists of a simple and efficient sliding window-based feature description framework and interactive re-ranking schemes, which are introduced because the characteristics of manga images are different from those of naturalistic images, and thus, traditional image retrieval methods are not effective. Additionally, the future directions of improving the current retrieval system, including by using a combination of text features and image features, and the construction of a large database are discussed. <s> BIB002
Free-hand sketch stands itself as a special data domain, and it has a lot of cross-modal applications with other modalities. In this section, sketch related cross-modal deep learning topics/models will be discussed, involving visual domains (e.g., natural photo, 3D shape, video) and textual domains. For each sketch-based cross-modal task, its task-specific challenges, typical deep networks, and applications will be introduced and compared. In particular, the sketch-based cross-modal retrieval and generation will be discussed in detail. Nowadays, most existing visual retrieval approaches work in the "query-by-example" (QBE) BIB001 framework that users provide examples of the content that they seek. Compared with the queries from other modalities (e.g., photo, video, text), sketch has its unique superiorities. In some scenarios, it's difficult/unpractical for user to provide the photos or videos to describe what object or content they want to search. Moreover, sometimes, users do not know the exact name of the object/content that they seek. In particular, editing suitable key words or textual description become more difficult, when users want to search some fine-grained contents. However, every user is able to sketch out the target object/content with the visual details as much as he/she like. Sketch can be used as query example to retrieve natural photo, manga BIB002 , 3D shape, video, etc. The advantage of sketch queries will become more significantly when users want to search something in fine-grained/instance-level.