3,835 research outputs found
Visually grounded learning of keyword prediction from untranscribed speech
During language acquisition, infants have the benefit of visual cues to
ground spoken language. Robots similarly have access to audio and visual
sensors. Recent work has shown that images and spoken captions can be mapped
into a meaningful common space, allowing images to be retrieved using speech
and vice versa. In this setting of images paired with untranscribed spoken
captions, we consider whether computer vision systems can be used to obtain
textual labels for the speech. Concretely, we use an image-to-words multi-label
visual classifier to tag images with soft textual labels, and then train a
neural network to map from the speech to these soft targets. We show that the
resulting speech system is able to predict which words occur in an
utterance---acting as a spoken bag-of-words classifier---without seeing any
parallel speech and text. We find that the model often confuses semantically
related words, e.g. "man" and "person", making it even more effective as a
semantic keyword spotter.Comment: 5 pages, 3 figures, 5 tables; small updates, added link to code;
accepted to Interspeech 201
Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos
We propose a new zero-shot Event Detection method by Multi-modal
Distributional Semantic embedding of videos. Our model embeds object and action
concepts as well as other available modalities from videos into a
distributional semantic space. To our knowledge, this is the first Zero-Shot
event detection model that is built on top of distributional semantics and
extends it in the following directions: (a) semantic embedding of multimodal
information in videos (with focus on the visual modalities), (b) automatically
determining relevance of concepts/attributes to a free text query, which could
be useful for other applications, and (c) retrieving videos by free text event
query (e.g., "changing a vehicle tire") based on their content. We embed videos
into a distributional semantic space and then measure the similarity between
videos and the event query in a free text form. We validated our method on the
large TRECVID MED (Multimedia Event Detection) challenge. Using only the event
title as a query, our method outperformed the state-of-the-art that uses big
descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC
metric. It is also an order of magnitude faster.Comment: To appear in AAAI 201
Automatic tagging and geotagging in video collections and communities
Automatically generated tags and geotags hold great promise
to improve access to video collections and online communi-
ties. We overview three tasks offered in the MediaEval 2010
benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features
Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning
In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians\u27 viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic.
As a prior study, we conducted data elicitation experiments, where physicians were instructed to inspect each medical image towards a diagnosis while describing image content to a student seated nearby. Experts\u27 eye movements and their verbal descriptions of the image content were recorded to capture various aspects of expert image understanding. This dissertation aims at an intuitive approach to extracting expert knowledge, which is to find patterns in expert data elicited from image-based diagnoses. These patterns are useful to understand both the characteristics of the medical images and the experts\u27 cognitive reasoning processes.
The transformation from the viewed raw image features to interpretation as domain-specific concepts requires experts\u27 domain knowledge and cognitive reasoning. This dissertation also approximates this transformation using a matrix factorization-based framework, which helps project multiple expert-derived data modalities to high-level abstractions.
To combine additional expert interventions with computational processing capabilities, an interactive machine learning paradigm is developed to treat experts as an integral part of the learning process. Specifically, experts refine medical image groups presented by the learned model locally, to incrementally re-learn the model globally. This paradigm avoids the onerous expert annotations for model training, while aligning the learned model with experts\u27 sense-making
Video Captioning with Guidance of Multimodal Latent Topics
The topic diversity of open-domain videos leads to various vocabularies and
linguistic expressions in describing video contents, and therefore, makes the
video captioning task even more challenging. In this paper, we propose an
unified caption framework, M&M TGM, which mines multimodal topics in
unsupervised fashion from data and guides the caption decoder with these
topics. Compared to pre-defined topics, the mined multimodal topics are more
semantically and visually coherent and can reflect the topic distribution of
videos better. We formulate the topic-aware caption generation as a multi-task
learning problem, in which we add a parallel task, topic prediction, in
addition to the caption task. For the topic prediction task, we use the mined
topics as the teacher to train a student topic prediction model, which learns
to predict the latent topics from multimodal contents of videos. The topic
prediction provides intermediate supervision to the learning process. As for
the caption task, we propose a novel topic-aware decoder to generate more
accurate and detailed video descriptions with the guidance from latent topics.
The entire learning procedure is end-to-end and it optimizes both tasks
simultaneously. The results from extensive experiments conducted on the MSR-VTT
and Youtube2Text datasets demonstrate the effectiveness of our proposed model.
M&M TGM not only outperforms prior state-of-the-art methods on multiple
evaluation metrics and on both benchmark datasets, but also achieves better
generalization ability.Comment: ACM Multimedia 201
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