2,372 research outputs found
Jointly Modeling Embedding and Translation to Bridge Video and Language
Automatically describing video content with natural language is a fundamental
challenge of multimedia. Recurrent Neural Networks (RNN), which models sequence
dynamics, has attracted increasing attention on visual interpretation. However,
most existing approaches generate a word locally with given previous words and
the visual content, while the relationship between sentence semantics and
visual content is not holistically exploited. As a result, the generated
sentences may be contextually correct but the semantics (e.g., subjects, verbs
or objects) are not true.
This paper presents a novel unified framework, named Long Short-Term Memory
with visual-semantic Embedding (LSTM-E), which can simultaneously explore the
learning of LSTM and visual-semantic embedding. The former aims to locally
maximize the probability of generating the next word given previous words and
visual content, while the latter is to create a visual-semantic embedding space
for enforcing the relationship between the semantics of the entire sentence and
visual content. Our proposed LSTM-E consists of three components: a 2-D and/or
3-D deep convolutional neural networks for learning powerful video
representation, a deep RNN for generating sentences, and a joint embedding
model for exploring the relationships between visual content and sentence
semantics. The experiments on YouTube2Text dataset show that our proposed
LSTM-E achieves to-date the best reported performance in generating natural
sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. We also
demonstrate that LSTM-E is superior in predicting Subject-Verb-Object (SVO)
triplets to several state-of-the-art techniques
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos
The task of video grounding, which temporally localizes a natural language
description in a video, plays an important role in understanding videos.
Existing studies have adopted strategies of sliding window over the entire
video or exhaustively ranking all possible clip-sentence pairs in a
pre-segmented video, which inevitably suffer from exhaustively enumerated
candidates. To alleviate this problem, we formulate this task as a problem of
sequential decision making by learning an agent which regulates the temporal
grounding boundaries progressively based on its policy. Specifically, we
propose a reinforcement learning based framework improved by multi-task
learning and it shows steady performance gains by considering additional
supervised boundary information during training. Our proposed framework
achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset
and Charades-STA dataset while observing only 10 or less clips per video.Comment: AAAI 201
The Long-Short Story of Movie Description
Generating descriptions for videos has many applications including assisting
blind people and human-robot interaction. The recent advances in image
captioning as well as the release of large-scale movie description datasets
such as MPII Movie Description allow to study this task in more depth. Many of
the proposed methods for image captioning rely on pre-trained object classifier
CNNs and Long-Short Term Memory recurrent networks (LSTMs) for generating
descriptions. While image description focuses on objects, we argue that it is
important to distinguish verbs, objects, and places in the challenging setting
of movie description. In this work we show how to learn robust visual
classifiers from the weak annotations of the sentence descriptions. Based on
these visual classifiers we learn how to generate a description using an LSTM.
We explore different design choices to build and train the LSTM and achieve the
best performance to date on the challenging MPII-MD dataset. We compare and
analyze our approach and prior work along various dimensions to better
understand the key challenges of the movie description task
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