2,357 research outputs found
Exploring Different Dimensions of Attention for Uncertainty Detection
Neural networks with attention have proven effective for many natural
language processing tasks. In this paper, we develop attention mechanisms for
uncertainty detection. In particular, we generalize standardly used attention
mechanisms by introducing external attention and sequence-preserving attention.
These novel architectures differ from standard approaches in that they use
external resources to compute attention weights and preserve sequence
information. We compare them to other configurations along different dimensions
of attention. Our novel architectures set the new state of the art on a
Wikipedia benchmark dataset and perform similar to the state-of-the-art model
on a biomedical benchmark which uses a large set of linguistic features.Comment: accepted at EACL 201
CentralNet: a Multilayer Approach for Multimodal Fusion
This paper proposes a novel multimodal fusion approach, aiming to produce
best possible decisions by integrating information coming from multiple media.
While most of the past multimodal approaches either work by projecting the
features of different modalities into the same space, or by coordinating the
representations of each modality through the use of constraints, our approach
borrows from both visions. More specifically, assuming each modality can be
processed by a separated deep convolutional network, allowing to take decisions
independently from each modality, we introduce a central network linking the
modality specific networks. This central network not only provides a common
feature embedding but also regularizes the modality specific networks through
the use of multi-task learning. The proposed approach is validated on 4
different computer vision tasks on which it consistently improves the accuracy
of existing multimodal fusion approaches
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-label
zero-shot learning (ML-ZSL), which is able to predict multiple unseen class
labels for each input instance. Inspired by the way humans utilize semantic
knowledge between objects of interests, we propose a framework that
incorporates knowledge graphs for describing the relationships between multiple
labels. Our model learns an information propagation mechanism from the semantic
label space, which can be applied to model the interdependencies between seen
and unseen class labels. With such investigation of structured knowledge graphs
for visual reasoning, we show that our model can be applied for solving
multi-label classification and ML-ZSL tasks. Compared to state-of-the-art
approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201
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