23,674 research outputs found
Revisiting Visual Question Answering Baselines
Visual question answering (VQA) is an interesting learning setting for
evaluating the abilities and shortcomings of current systems for image
understanding. Many of the recently proposed VQA systems include attention or
memory mechanisms designed to support "reasoning". For multiple-choice VQA,
nearly all of these systems train a multi-class classifier on image and
question features to predict an answer. This paper questions the value of these
common practices and develops a simple alternative model based on binary
classification. Instead of treating answers as competing choices, our model
receives the answer as input and predicts whether or not an
image-question-answer triplet is correct. We evaluate our model on the Visual7W
Telling and the VQA Real Multiple Choice tasks, and find that even simple
versions of our model perform competitively. Our best model achieves
state-of-the-art performance on the Visual7W Telling task and compares
surprisingly well with the most complex systems proposed for the VQA Real
Multiple Choice task. We explore variants of the model and study its
transferability between both datasets. We also present an error analysis of our
model that suggests a key problem of current VQA systems lies in the lack of
visual grounding of concepts that occur in the questions and answers. Overall,
our results suggest that the performance of current VQA systems is not
significantly better than that of systems designed to exploit dataset biases.Comment: European Conference on Computer Visio
Evaluating the Representational Hub of Language and Vision Models
The multimodal models used in the emerging field at the intersection of
computational linguistics and computer vision implement the bottom-up
processing of the `Hub and Spoke' architecture proposed in cognitive science to
represent how the brain processes and combines multi-sensory inputs. In
particular, the Hub is implemented as a neural network encoder. We investigate
the effect on this encoder of various vision-and-language tasks proposed in the
literature: visual question answering, visual reference resolution, and
visually grounded dialogue. To measure the quality of the representations
learned by the encoder, we use two kinds of analyses. First, we evaluate the
encoder pre-trained on the different vision-and-language tasks on an existing
diagnostic task designed to assess multimodal semantic understanding. Second,
we carry out a battery of analyses aimed at studying how the encoder merges and
exploits the two modalities.Comment: Accepted to IWCS 201
Neural blackboard architectures of combinatorial structures in cognition
Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception
Embodied Question Answering
We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA.Comment: 20 pages, 13 figures, Webpage: https://embodiedqa.org
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