Multimodal Reranking of Content-based Recommendations for Hyperlinking Video Snippets
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In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts ...
Multimedia databases are growing rapidly in size in the digital age. To increase the value of these data and to enhance the user experience, there is a need to make these videos searchable through automatic indexing. Because people appearing and talking in ...
EPFL2019
We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic ...
This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt ...
Recommendation systems help users identify interesting content, but they also open new privacy threats. In this paper, we deeply analyze the effect of a Sybil attack that tries to infer information on users from a user-based collaborative-filtering recomme ...
We propose a method for extractive summarization of audiovisual recordings focusing on topic-level segments. We first build a content similarity graph between all segments across the collection, using word vectors from the transcripts, and then select the ...
This report presents a study on assisting users in building queries to perform real-time searches in a news and social media monitoring system. The system accepts complex queries, and we assist the user by suggesting related keywords or entities. We do thi ...
Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...
This paper introduces a query refinement method applied to questions asked by users to a system during a meeting or a conversation that they have with other users. To answer the questions, the proposed method leverages the local context of the conversation ...
Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...