Query: movie trivia

title: MovieNet: A Holistic Dataset for Movie Understanding
abstract: Recent years have seen remarkable advances in visual understanding. However, how to understand a story based long video with artistic styles, e.g. movie, remains challenging. In this paper, we introduce MovieNet a holistic dataset for movie understanding. MovieNet contains 1,100 movies with a large amount of multi modal data, e.g. trailers, photos, plot descriptions, etc. Besides, different aspects of manual annotations are provided in MovieNet, including 1.1M characters with bounding boxes and identities, 42K scene boundaries, 2.5K aligned description sentences, 65K tags of place and action, and 92K tags of cinematic style. To the best of our knowledge, MovieNet is the largest dataset with richest annotations for comprehensive movie understanding. Based on MovieNet, we set up several benchmarks for movie understanding from different angles. Extensive experiments are executed on these benchmarks to show the immeasurable value of MovieNet and the gap of current approaches towards comprehensive movie understanding. We believe that such a holistic dataset would promote the researches on story based long video understanding and beyond. MovieNet will be published in compliance with regulations at this https URL.

title: What Question Answering can Learn from Trivia Nerds
abstract: In addition to the traditional task of machines answering questions, question answering (QA) research creates interesting, challenging questions that help systems how to answer questions and reveal the best systems. We argue that creating a QA dataset and the ubiquitous leaderboard that goes with it closely resembles running a trivia tournament: you write questions, have agents (either humans or machines) answer the questions, and declare a winner. However, the research community has ignored the hard learned lessons from decades of the trivia community creating vibrant, fair, and effective question answering competitions. After detailing problems with existing QA datasets, we outline the key lessons removing ambiguity, discriminating skill, and adjudicating disputes that can transfer to QA research and how they might be implemented.

title: A Local to Global Approach to Multi Modal Movie Scene Segmentation
abstract: Scene, as the crucial unit of storytelling in movies, contains complex activities of actors and their interactions in a physical environment. Identifying the composition of scenes serves as a critical step towards semantic understanding of movies. This is very challenging compared to the videos studied in conventional vision problems, e.g. action recognition, as scenes in movies usually contain much richer temporal structures and more complex semantic information. Towards this goal, we scale up the scene segmentation task by building a large scale video dataset MovieScenes, which contains 21K annotated scene segments from 150 movies. We further propose a local to global scene segmentation framework, which integrates multi modal information across three levels, i.e. clip, segment, and movie. This framework is able to distill complex semantics from hierarchical temporal structures over a long movie, providing top down guidance for scene segmentation. Our experiments show that the proposed network is able to segment a movie into scenes with high accuracy, consistently outperforming previous methods. We also found that pretraining on our MovieScenes can bring significant improvements to the existing approaches.

title: Hierarchical Trivia Fact Extraction from Wikipedia Articles
abstract: Recently, automatic trivia fact extraction has attracted much research interest. Modern search engines have begun to provide trivia facts as the information for entities because they can motivate more user engagement. In this paper, we propose a new unsupervised algorithm that automatically mines trivia facts for a given entity. Unlike previous studies, the proposed algorithm targets at a single Wikipedia article and leverages its hierarchical structure via top down processing. Thus, the proposed algorithm offers two distinctive advantages: it does not incur high computation time, and it provides a domain independent approach for extracting trivia facts. Experimental results demonstrate that the proposed algorithm is over 100 times faster than the existing method which considers Wikipedia categories. Human evaluation demonstrates that the proposed algorithm can mine better trivia facts regardless of the target entity domain and outperforms the existing methods.

title: Progressive Attention Memory Network for Movie Story Question Answering
abstract: This paper proposes the progressive attention memory network (PAMN) for movie story question answering (QA) Movie story QA is challenging compared to VQA in two aspects: (1) pinpointing the temporal parts relevant to answer the question is difficult as the movies are typically longer than an hour, (2) it has both video and subtitle where different questions require different modality to infer the answer. To overcome these challenges, PAMN involves three main features: (1) progressive attention mechanism that utilizes cues from both question and answer to progressively prune out irrelevant temporal parts in memory, (2) dynamic modality fusion that adaptively determines the contribution of each modality for answering the current question, and (3) belief correction answering scheme that successively corrects the prediction score on each candidate answer. Experiments on publicly available benchmark datasets, MovieQA and TVQA, demonstrate that each feature contributes to our movie story QA architecture, PAMN, and improves performance to achieve the state of the art result. Qualitative analysis by visualizing the inference mechanism of PAMN is also provided.

title: Edutrivias: Web support system based on trivia for the teaching learning process
abstract: The e learning systems allow users to improve the traditional education systems granting flexibility, permanence, and synchrony. Many e learning systems are based on the use of web applications and games. A trivia quiz is a game metaphor which allows to enhance the teaching learning process in an entertaining, constant and motivating way for students. This work presents Edutrivias, a web system created to support the teaching learning process through trivia quizzes. Edutrivias uses the trivia quiz as an educational resource that allows positive reinforcement, monitoring, control and effective feedback of student learning. The description of the web system and an exploratory case study are presented. Preliminary results show that Edutrivias generates a positive impact on students, encouraging self learning and supporting knowledge retention.

title: Genre Classification of Movie Trailers using 3D Convolutional Neural Networks
abstract: There is a constant influx of movies every year in the entertainment industry. A crucial ingredient that helps movies succeed at the box office is its trailer. A trailer helps a viewer form an opinion about the movie and is often an accurate representation of the movie. Thus, genre identification of movie trailers is a significant problem that can be solved for movie categorization and censorship. In this paper, we propose using a 3D CNN for solving this problem. The advantage of 3D CNN is its ability to capture the spatial as well as the temporal information present in a movie trailer. We achieve an accuracy of 82.14% which is greater than other models that utilize just 2D CNNs.

title: Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches Using Ensemble Learning Voted Algorithm
abstract: In this paper Authors explained their experimental works. In this work we choose movies review from corpora community. corpora contain numbers of dataset from where we selected movies review. In this dataset we have two categories negative positive each category contains 1000 files. In our file we have movies review by different reviewer. we took. txt file extension. Here we are finding sentiment polarity against those movies. we extend some more in comparison to our base papers. we apply numbers of classification algorithm like NaiveBayes, SkLearn, Support Vector Machine. We applied multiple classifier like MultinomialNB, GaussianNB, BernoulliNB and many more. we select different features (in count or numbers) to understand Accuracy Level of different Algorithms. Here Authors used Ensemble Learning where we combined number of classification algorithms and try voted algorithm to find best accuracy.

title: Movie Recommender System Using K Means Clustering AND K Nearest Neighbor
abstract: In the field of Artificial Intelligence Machine learning provides the automatic systems which learn and improve itself from experience without being explicitly programmed. In this research work a movie recommender system is built using the K Means Clustering and K Nearest Neighbor algorithms. The movielens dataset is taken from kaggle. The system is implemented in python programming language. The proposed work deals with the introduction of various concepts related to machine learning and recommendation system. In this work, various tools and techniques have been used to build recommender systems. Various algorithms such as K Means Clustering, KNN, Collaborative Filtering, Content Based Filtering have been described in detail. Further, after studying different types of machine learning algorithms, there is a clear picture of where to apply which algorithm in different areas of industries such as recommender systems, e commerce, etc. Then there is an illustration of how implementations and working of the proposed system are used for the implementation of the movie recommender system. Various building blocks of the proposed system such as Architecture, Process Flow, Pseudo Code, Implementation and Working of the System is described in detail. Finally, in this work for different cluster values, different values of Root Mean Squared Error are obtained. In this proposed work as the no of clusters decreases, the value of RMSE also decreases. The best value of RMSE obtained is 1.081648. The results given by the proposed system are better than the existing technique on the basis of RMSE value.

title: Learning Interactions and Relationships Between Movie Characters
abstract: 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.