Towards textually describing complex video contents with audio-visual concept classifiers
By Chun-Chet Tan, Yu-Gang Jiang, Chong-Wah Ngo. 2011
43 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Multimodal Learning, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Bernt Schiele
- Yi Yang
- Dacheng Tao
- Andrew Zisserman
- Xiaogang Wang
- Ming-Hsuan Yang
In Multimodal Learning
- Trevor Darrell
- Marcus Rohrbach
- Kate Saenko
- Lisa Anne Hendricks
- Anna Rohrbach
- Mohit Bansal
- Subhashini Venugopalan
- Jeff Donahue
In Signal Processing
- Marcus Rohrbach
- Rainer Stiefelhagen
- Makarand Tapaswi
- Martin Bäuml
- Eric P. Xing
- Subhabrata Bhattacharya
- Ivan Titov
- Xiaojun Chang
Citations in the corpus
- Long-term recurrent convolutional networks for visual recognition and description (2014, methodology): “[11, 30, 17, 3, 6, 17, 41, 42] propose methods for generating sentence descriptions for video, but to our knowledge we present the first application of deep models to the video description task.”
- A dataset for Movie Description (2015, background): “In recent years there has been an increased interest in automatically describing images [23, 39, 40, 50, 45, 40, 41, 34, 61, 22] and videos [37, 27, 8, 28, 32, 62, 16, 26, 64, 55] with natural language.”
- Translating Video Content to Natural Language Descriptions (2013, methodology): “[26] learns audio-visual concepts and generates a video description for three different activities using rules to combine action, scene, and audio concepts with glue words.”
- Long-Term Recurrent Convolutional Networks for Visual Recognition and Description (2017, methodology): “Recent approaches to describing video with natural language have made use of templates, retrieval, or language models [11], [59], [60], [60], [61], [62], [63], [64].”
- Visual to Text: Survey of Image and Video Captioning (2019, background): “Second, with a few exceptions [106], many existing approaches are focusing on short duration videos or video clips which can be described in a couple of sentences [29], [39], [41].”
- Semantic Pooling for Complex Event Analysis in Untrimmed Videos (2016, background): “[48] proposed a rule-based recounting approach to collect the evidence, involving human knowledge heavily.”
- High-level event recognition in unconstrained videos (2012, methodology): “Most works on textual recounting used subjective user studies [134,105].”
- Aligning plot synopses to videos for story-based retrieval (2015, methodology): “[41] use audio-visual concept classifiers and rule-based methods to generate descriptions for a few set of handpicked concepts.”