Objects that Sound

In this paper our objectives are, first, networks that can embed audio and visual inputs into a common space that is suitable for cross-modal retrieval; and second, a network that can localize the object that sounds in an image, given the audio signal. We achieve both these objectives by training from unlabelled video using only audio-visual correspondence (AVC) as the objective function. This is a form of cross-modal self-supervision from video. To this end, we design new network architectures that can be trained for cross-modal retrieval and localizing the sound source in an image, by using the AVC task. We make the following contributions: (i) show that audio and visual embeddings can be learnt that enable both within-mode (e.g. audio-to-audio) and between-mode retrieval; (ii) explore various architectures for the AVC task, including those for the visual stream that ingest a single image, or multiple images, or a single image and multi-frame optical flow; (iii) show that the semantic object that sounds within an image can be localized (using only the sound, no motion or flow information); and (iv) give a cautionary tale on how to avoid undesirable shortcuts in the data preparation.Comment: Appears in: European Conference on Computer Vision (ECCV) 201

Look, Listen and Learn

We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos? There is a valuable, but so far untapped, source of information contained in the video itself -- the correspondence between the visual and the audio streams, and we introduce a novel "Audio-Visual Correspondence" learning task that makes use of this. Training visual and audio networks from scratch, without any additional supervision other than the raw unconstrained videos themselves, is shown to successfully solve this task, and, more interestingly, result in good visual and audio representations. These features set the new state-of-the-art on two sound classification benchmarks, and perform on par with the state-of-the-art self-supervised approaches on ImageNet classification. We also demonstrate that the network is able to localize objects in both modalities, as well as perform fine-grained recognition tasks.Comment: Appears in: IEEE International Conference on Computer Vision (ICCV) 201

Multimodal non-linear latent semantic method for information retrieval

La búsqueda y recuperación de datos multimodales es una importante tarea dentro del campo de búsqueda y recuperación de información, donde las consultas y los elementos de la base de datos objetivo están representados por un conjunto de modalidades, donde cada una de ellas captura un aspecto de un fenómeno de interés. Cada modalidad contiene información complementaria y común a otras modalidades. Con el fin de tomar ventaja de la información adicional distribuida a través de las distintas modalidades han sido desarrollados muchos algoritmos y métodos que utilizan las propiedades estadísticas en los datos multimodales para encontrar correlaciones implícitas, otros aprenden a calcular distancias heterogéneas, otros métodos aprenden a proyectar los datos desde el espacio de entrada hasta un espacio semántico común, donde las diferentes modalidades son comparables y se puede construir un ranking a partir de ellas. En esta tesis se presenta el diseño de un sistema para la búsqueda y recuperación de información multimodal que aprende varias proyecciones no lineales a espacios semánticos latentes donde las distintas modalidades son representadas en conjunto y es posible realizar comparaciones y medidas de similitud para construir rankings multimodales. Adicionalmente se propone un método kernelizado para la proyección de datos a un espacio semántico latente usando la información de las etiquetas como método de supervisión para construir índice multimodal que integra los datos multimodales y la información de las etiquetas; este método puede proyectar los datos a tres diferentes espacios semánticos donde varias configuraciones de búsqueda y recuperación de información pueden ser aplicadas. El sistema y el método propuestos fueron evaluados en un conjunto de datos compuesto por casos médicos, donde cada caso consta de una imagen de tejido prostático, un reporte de texto del patólogo y un valor de Gleason score como etiqueta de supervisión. Combinando la información multimodal y la información en las etiquetas se generó un índice multimodal que se utilizó para realizar la tarea de búsqueda y recuperación de información por contenido obteniendo resultados sobresalientes. Las proyecciones no-lineales permiten al modelo una mayor flexibilidad y capacidad de representación. Sin embargo calcular estas proyecciones no-lineales en un conjunto de datos enorme es computacionalmente costoso, para reducir este costo y habilitar el modelo para procesar datos a gran escala, la técnica del budget fue utilizada, mostrando un buen compromiso entre efectividad y velocidad.Multimodal information retrieval is an information retrieval sub-task where queries and database target elements are composed of several modalities or views. A modality is a representation of complex phenomena, captured and measured by different sensors or information sources, each one encodes some information about it. Each modality representation contains complementary and shared information about the phenomenon of interest, this additional information can be used to improve the information retrieval process. Several methods have been developed to take advantage of additional information distributed across different modalities. Some of them exploit statistical properties in multimodal data to find correlations and implicit relationships, others learn heterogeneous distance functions, and others learn linear and non-linear projections that transform data from the original input space to a common latent semantic space where different modalities are comparable. In spite of the attention dedicated to this issue, multimodal information retrieval is still an open problem. This thesis presents a multimodal information retrieval system designed to learn several mapping functions to transform multimodal data to a latent semantic space, where different modalities are combined and can be compared to build a multimodal ranking and perform a multimodal information retrieval task. Additionally, a multimodal kernelized latent semantic embedding method is proposed to construct a supervised multimodal index, integrating multimodal data and label supervision. This method can perform mappings to three different spaces where some information retrieval task setups can be performed. The proposed system and method were evaluated in a multimodal medical case-based retrieval task where data is composed of whole-slide images of prostate tissue samples, pathologist’s text report and Gleason score as a supervised label. Multimodal data and labels were combined to produce a multimodal index. This index was used to retrieve multimodal information and achieves outstanding results compared with previous works on this topic. Non-linear mappings provide more flexibility and representation capacity to the proposed model. However, constructing the non-linear mapping in a large dataset using kernel methods can be computationally costly. To reduce the cost and allow large scale applications, the budget technique was introduced, showing good performance between speed and effectiveness.COLCIENCIASJóvenes investigadores 761/2016Línea de investigación: Ciencias de la computaciónMaestrí