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Found 14 papers, 4 papers with code
Improved optimization strategies for deep Multi-Task Networks
In Multi-Task Learning (MTL), it is a common practice to train multi-task networks by optimizing an objective function, which is a weighted average of the task-specific objective functions.
Maximum Roaming Multi-Task Learning
Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance.
Serial Speakers: a Dataset of TV Series
For over a decade, TV series have been drawing increasing interest, both from the audience and from various academic fields.
Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training
In recent years, representation learning approaches have disrupted many multimedia computing tasks.
Remembering Winter Was Coming: Character-Oriented Video Summaries of TV Series
In this paper, we tackle plot modeling by considering the social network of interactions between the characters involved in the narrative: substantial, durable changes in a major character's social environment suggest a new development relevant for the summary.
Extraction and Analysis of Fictional Character Networks: A Survey
We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors.
M2H-GAN: A GAN-based Mapping from Machine to Human Transcripts for Speech Understanding
TRS transcripts are only used to measure the performances of ASR systems.
Generative Adversarial Network
Spoken Language Understanding
Multiple topic identification in telephone conversations
Furthermore, using the theme skeleton of a conversation from which thematic densities are derived, it will be possible to extract components of an automatic conversation report to be used for improving the service performance.
Audiovisual speaker diarization of TV series
Speaker diarization may be difficult to achieve when applied to narrative films, where speakers usually talk in adverse acoustic conditions: background music, sound effects, wide variations in intonation may hide the inter-speaker variability and make audio-based speaker diarization approaches error prone.
Constrained speaker diarization of TV series based on visual patterns
Speaker diarization, usually denoted as the ''who spoke when'' task, turns out to be particularly challenging when applied to fictional films, where many characters talk in various acoustic conditions (background music, sound effects...).
D{é}tection de locuteurs dans les s{é}ries TV
Speaker diarization of audio streams turns out to be particularly challenging when applied to fictional films, where many characters talk in various acoustic conditions (background music, sound effects, variations in intonation...).
Systèmes du LIA à DEFT'13
The 2013 D\'efi de Fouille de Textes (DEFT) campaign is interested in two types of language analysis tasks, the document classification and the information extraction in the specialized domain of cuisine recipes.
Narrative Smoothing: Dynamic Conversational Network for the Analysis of TV Series Plots
In order to assess our method, we apply it to a new corpus of 3 popular TV series, and compare it to both standard approaches.
How to merge three different methods for information filtering ?
Twitter is now a gold marketing tool for entities concerned with online reputation.
