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For example, some of them relax the binary constraints to generate the hash codes, which may generate large quantization error. Although some discrete schemes have been proposed, most of them are time-consuming. Segmenting video content into events provides semantic structures for indexing, retrieval, and summarization. Since motion cues are not available in continuous photo-streams, and annotations in lifelogging are scarce and costly, the frames are usually clustered into events by comparing the visual features between them in an unsupervised way.
However, such methodologies are ineffective to deal with heterogeneous events, e. Although image-to-image translation has been widely studied, the video-to-video translation is rarely mentioned. In this paper, we propose an unified video-to-video translation framework to accom- plish different tasks, like video super-resolution, video colouriza- tion, and video segmentation, etc. A consequent question within video-to-video translation lies in the flickering appearance along with the varying frames. Multi-view learning has shown its powerful potential in many applications and achieved outstanding performances compared with the single-view based methods.
Weakly supervised temporal action detection is a Herculean task in understanding untrimmed videos, since no supervisory signal except the video-level category label is available on training data. Under the supervision of category labels, weakly supervised detectors are usually built upon classifiers. Human parsing is an important task in human-centric analysis. Despite the remarkable progress in single-human parsing, the more realistic case of multi-human parsing remains challenging in terms of the data and the model. Compared with the considerable number of available single-human parsing datasets, the datasets for multi-human parsing are very limited in number mainly due to the huge annotation effort required. Given only a few image-text pairs, humans can learn to detect semantic concepts and describe the content. For machine learning algorithms, they usually require a lot of data to train a deep neural network to solve the problem.