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Found 6 papers, 2 papers with code
Action Classification and Highlighting in Videos
Inspired by recent advances in neural machine translation, that jointly align and translate using encoder-decoder networks equipped with attention, we propose an attentionbased LSTM model for human activity recognition.
Learning Language-Visual Embedding for Movie Understanding with Natural-Language
We evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1) Standard Ranking for video annotation and retrieval 2) Our proposed movie multiple-choice test.
Ranked #32 on
Video Retrieval
on MSR-VTT
Movie Description
In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions.
Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research
DVS is an audio narration describing the visual elements and actions in a movie for the visually impaired.
Describing Videos by Exploiting Temporal Structure
In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.
A Bag of Words Approach for Semantic Segmentation of Monitored Scenes
This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera.
