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Found 10 papers, 3 papers with code
Memory Consolidation Enables Long-Context Video Understanding
Most transformer-based video encoders are limited to short temporal contexts due to their quadratic complexity.
A Simple Recipe for Contrastively Pre-training Video-First Encoders Beyond 16 Frames
Understanding long, real-world videos requires modeling of long-range visual dependencies.
Hierarchical3D Adapters for Long Video-to-text Summarization
In this paper, we focus on video-to-text summarization and investigate how to best utilize multimodal information for summarizing long inputs (e. g., an hour-long TV show) into long outputs (e. g., a multi-sentence summary).
Towards Coherent and Consistent Use of Entities in Narrative Generation
Large pre-trained language models (LMs) have demonstrated impressive capabilities in generating long, fluent text; however, there is little to no analysis on their ability to maintain entity coherence and consistency.
Film Trailer Generation via Task Decomposition
Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie.
Movie Summarization via Sparse Graph Construction
We summarize full-length movies by creating shorter videos containing their most informative scenes.
Screenplay Summarization Using Latent Narrative Structure
Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront.
Movie Plot Analysis via Turning Point Identification
According to screenwriting theory, turning points (e. g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e. g., setup, complications, aftermath).
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets".
Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter
In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter.
