Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Argaw, Dawit Mureja"'
Autor:
Argaw, Dawit Mureja, Soldan, Mattia, Pardo, Alejandro, Zhao, Chen, Heilbron, Fabian Caba, Chung, Joon Son, Ghanem, Bernard
Movie trailers are an essential tool for promoting films and attracting audiences. However, the process of creating trailers can be time-consuming and expensive. To streamline this process, we propose an automatic trailer generation framework that ge
Externí odkaz:
http://arxiv.org/abs/2404.03477
Autor:
Argaw, Dawit Mureja, Yoon, Seunghyun, Heilbron, Fabian Caba, Deilamsalehy, Hanieh, Bui, Trung, Wang, Zhaowen, Dernoncourt, Franck, Chung, Joon Son
Long-form video content constitutes a significant portion of internet traffic, making automated video summarization an essential research problem. However, existing video summarization datasets are notably limited in their size, constraining the effe
Externí odkaz:
http://arxiv.org/abs/2404.03398
Existing video compression (VC) methods primarily aim to reduce the spatial and temporal redundancies between consecutive frames in a video while preserving its quality. In this regard, previous works have achieved remarkable results on videos acquir
Externí odkaz:
http://arxiv.org/abs/2311.04430
Learning computer vision models from (and for) movies has a long-standing history. While great progress has been attained, there is still a need for a pretrained multimodal model that can perform well in the ever-growing set of movie understanding ta
Externí odkaz:
http://arxiv.org/abs/2308.09775
Machine learning is transforming the video editing industry. Recent advances in computer vision have leveled-up video editing tasks such as intelligent reframing, rotoscoping, color grading, or applying digital makeups. However, most of the solutions
Externí odkaz:
http://arxiv.org/abs/2207.09812
Autor:
Argaw, Dawit Mureja, Kweon, In So
Video frame interpolation (VFI) works generally predict intermediate frame(s) by first estimating the motion between inputs and then warping the inputs to the target time with the estimated motion. This approach, however, is not optimal when the temp
Externí odkaz:
http://arxiv.org/abs/2203.15427
We propose a novel framework to generate clean video frames from a single motion-blurred image. While a broad range of literature focuses on recovering a single image from a blurred image, in this work, we tackle a more challenging task i.e. video re
Externí odkaz:
http://arxiv.org/abs/2104.09134
In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in fundamental computer vision problems. In this work, we propose a nove
Externí odkaz:
http://arxiv.org/abs/2103.02996
Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and temporal upsampling. A broad range of research attempted to recover clean f
Externí odkaz:
http://arxiv.org/abs/2103.02984
Autor:
Zhang, Chaoning, Benz, Philipp, Argaw, Dawit Mureja, Lee, Seokju, Kim, Junsik, Rameau, Francois, Bazin, Jean-Charles, Kweon, In So
ResNet or DenseNet? Nowadays, most deep learning based approaches are implemented with seminal backbone networks, among them the two arguably most famous ones are ResNet and DenseNet. Despite their competitive performance and overwhelming popularity,
Externí odkaz:
http://arxiv.org/abs/2010.12496