Learning Interactions and Relationships between Movie Characters

CVPR 2020 (Oral)International audienceInteractions between people are often governed by their relationships. On the flip side, social relationships are built upon several interactions. Two strangers are more likely to greet and introduce themselves while becoming friends over time. We are fascinated by this interplay between interactions and relationships, and believe that it is an important aspect of understanding social situations. In this work, we propose neural models to learn and jointly predict interactions, relationships, and the pair of characters that are involved. We note that interactions are informed by a mixture of visual and dialog cues, and present a multimodal architecture to extract meaningful information from them. Localizing the pair of interacting characters in video is a time-consuming process, instead, we train our model to learn from clip-level weak labels. We evaluate our models on the MovieGraphs dataset and show the impact of modalities, use of longer temporal context for predicting relationships, and achieve encouraging performance using weak labels as compared with ground-truth labels. Code is online

Learning Interactions and Relationships between Movie Characters

Interactions between people are often governed by their relationships. On the flip side, social relationships are built upon several interactions. Two strangers are more likely to greet and introduce themselves while becoming friends over time. We are fascinated by this interplay between interactions and relationships, and believe that it is an important aspect of understanding social situations. In this work, we propose neural models to learn and jointly predict interactions, relationships, and the pair of characters that are involved. We note that interactions are informed by a mixture of visual and dialog cues, and present a multimodal architecture to extract meaningful information from them. Localizing the pair of interacting characters in video is a time-consuming process, instead, we train our model to learn from clip-level weak labels. We evaluate our models on the MovieGraphs dataset and show the impact of modalities, use of longer temporal context for predicting relationships, and achieve encouraging performance using weak labels as compared with ground-truth labels. Code is online.Comment: CVPR 2020 (Oral

Inferring Interpersonal Relations in Narrative Summaries

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation

Race, Gender, and Research: Implications for Teaching from Depictions of Professors in Popular Film, 1985-2005

When students enter college classrooms for the first time they inevitably have preconceived images of professors. According to research on student evaluations of teaching, these preconceptions have important implications in college classrooms. This study explores one avenue through which these preconceptions are perpetuated – popular film. Using content analysis we examine popular films released between 1985 and 2005 that contain professors in either primary or secondary roles. Our findings show stereotypical depictions beyond glasses, bow ties, and tweed jackets. Specifically, we find stereotypical images of race and gender as well as an emphasis on the importance of research, sometimes at the expense of teaching or ethical behavior. This research provides instructors with knowledge of the stereotypes that students may have upon entering the college classroom, which may impact classroom interactions and provides insight into how race and gender affect student evaluations of professors

Poofy Dresses and Big Guns: A poststructuralist analysis of gendered positioning through talk amongst friends

This article uses data collected from a class of eight to nine year-olds to show the specific ways children are defining their gendered positions within the context of their same-sex friendship groups. Children‘s subjectivities are described as both actively formed but also positioned within the surrounding (gendered) discourses. This article will show specific ways that structure and agency is played out through talk amongst friends. Importantly, the analysis of the talk indicates that children are able to both align themselves as well as challenge dominant gendered discourses. The article argues that informal talk amongst friends is an important space for children to make sense of masculinities and femininities and to develop their identities, particularly in the context of schools

The Cinderella Complex: Word Embeddings Reveal Gender Stereotypes in Movies and Books

Our analysis of thousands of movies and books reveals how these cultural products weave stereotypical gender roles into morality tales and perpetuate gender inequality through storytelling. Using the word embedding techniques, we reveal the constructed emotional dependency of female characters on male characters in stories

Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs

Conversational participants tend to immediately and unconsciously adapt to each other's language styles: a speaker will even adjust the number of articles and other function words in their next utterance in response to the number in their partner's immediately preceding utterance. This striking level of coordination is thought to have arisen as a way to achieve social goals, such as gaining approval or emphasizing difference in status. But has the adaptation mechanism become so deeply embedded in the language-generation process as to become a reflex? We argue that fictional dialogs offer a way to study this question, since authors create the conversations but don't receive the social benefits (rather, the imagined characters do). Indeed, we find significant coordination across many families of function words in our large movie-script corpus. We also report suggestive preliminary findings on the effects of gender and other features; e.g., surprisingly, for articles, on average, characters adapt more to females than to males.Comment: data available at http://www.cs.cornell.edu/~cristian/movie