55 research outputs found
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
Different from the traditional classification tasks which assume mutual
exclusion of labels, hierarchical multi-label classification (HMLC) aims to
assign multiple labels to every instance with the labels organized under
hierarchical relations. Besides the labels, since linguistic ontologies are
intrinsic hierarchies, the conceptual relations between words can also form
hierarchical structures. Thus it can be a challenge to learn mappings from word
hierarchies to label hierarchies. We propose to model the word and label
hierarchies by embedding them jointly in the hyperbolic space. The main reason
is that the tree-likeness of the hyperbolic space matches the complexity of
symbolic data with hierarchical structures. A new Hyperbolic Interaction Model
(HyperIM) is designed to learn the label-aware document representations and
make predictions for HMLC. Extensive experiments are conducted on three
benchmark datasets. The results have demonstrated that the new model can
realistically capture the complex data structures and further improve the
performance for HMLC comparing with the state-of-the-art methods. To facilitate
future research, our code is publicly available
Learning Visual Question Answering by Bootstrapping Hard Attention
Attention mechanisms in biological perception are thought to select subsets
of perceptual information for more sophisticated processing which would be
prohibitive to perform on all sensory inputs. In computer vision, however,
there has been relatively little exploration of hard attention, where some
information is selectively ignored, in spite of the success of soft attention,
where information is re-weighted and aggregated, but never filtered out. Here,
we introduce a new approach for hard attention and find it achieves very
competitive performance on a recently-released visual question answering
datasets, equalling and in some cases surpassing similar soft attention
architectures while entirely ignoring some features. Even though the hard
attention mechanism is thought to be non-differentiable, we found that the
feature magnitudes correlate with semantic relevance, and provide a useful
signal for our mechanism's attentional selection criterion. Because hard
attention selects important features of the input information, it can also be
more efficient than analogous soft attention mechanisms. This is especially
important for recent approaches that use non-local pairwise operations, whereby
computational and memory costs are quadratic in the size of the set of
features.Comment: ECCV 201
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