6,659 research outputs found
Visual Dialogue State Tracking for Question Generation
GuessWhat?! is a visual dialogue task between a guesser and an oracle. The
guesser aims to locate an object supposed by the oracle oneself in an image by
asking a sequence of Yes/No questions. Asking proper questions with the
progress of dialogue is vital for achieving successful final guess. As a
result, the progress of dialogue should be properly represented and tracked.
Previous models for question generation pay less attention on the
representation and tracking of dialogue states, and therefore are prone to
asking low quality questions such as repeated questions. This paper proposes
visual dialogue state tracking (VDST) based method for question generation. A
visual dialogue state is defined as the distribution on objects in the image as
well as representations of objects. Representations of objects are updated with
the change of the distribution on objects. An object-difference based attention
is used to decode new question. The distribution on objects is updated by
comparing the question-answer pair and objects. Experimental results on
GuessWhat?! dataset show that our model significantly outperforms existing
methods and achieves new state-of-the-art performance. It is also noticeable
that our model reduces the rate of repeated questions from more than 50% to
21.9% compared with previous state-of-the-art methods.Comment: 8 pages, 4 figures, Accept-Oral by AAAI-202
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat
We propose a grounded dialogue state encoder which addresses a foundational
issue on how to integrate visual grounding with dialogue system components. As
a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal
is to identify an object in a complex visual scene by asking a sequence of
yes/no questions. Our visually-grounded encoder leverages synergies between
guessing and asking questions, as it is trained jointly using multi-task
learning. We further enrich our model via a cooperative learning regime. We
show that the introduction of both the joint architecture and cooperative
learning lead to accuracy improvements over the baseline system. We compare our
approach to an alternative system which extends the baseline with reinforcement
learning. Our in-depth analysis shows that the linguistic skills of the two
models differ dramatically, despite approaching comparable performance levels.
This points at the importance of analyzing the linguistic output of competing
systems beyond numeric comparison solely based on task success.Comment: Accepted to NAACL 201
Markerless Motion Capture in the Crowd
This work uses crowdsourcing to obtain motion capture data from video
recordings. The data is obtained by information workers who click repeatedly to
indicate body configurations in the frames of a video, resulting in a model of
2D structure over time. We discuss techniques to optimize the tracking task and
strategies for maximizing accuracy and efficiency. We show visualizations of a
variety of motions captured with our pipeline then apply reconstruction
techniques to derive 3D structure.Comment: Presented at Collective Intelligence conference, 2012
(arXiv:1204.2991
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