*** CANCELLED***
Kristen Grauman
Wednesday 27th May 2015
Time: 4.00pm
Basement Seminar Room
Alexandra House, 17 Queen Square, London, WC1N 3AR
Learning the right thing with visual attributes
Visual attributes are human-nameable semantic properties. They are the
adjectives of the visual recognition world, capturing anything from
material properties (“metallic”, “furry”), shapes (“flat”, “boxy”),
expressions (“smiling”, “surprised”), to functions (“sittable”,
“drinkable”). An attribute may be a binary predicate (“shiny”) or a
relative comparison (“shinier than”). Many promising applications of
visual attributes---including zero-shot learning and image
search---demand that the vision system model the correct concept
precisely. However, existing methods are prone to learning the wrong
thing. In particular, the standard discriminative learning pipeline
tends to learn correlated properties, fails to account for differences
in human perception, and is inadequate to capture fine-grained attribute
differences.
I will present our work investigating how to “learn the right thing”
when training attribute models. First, to reduce confusions from
correlated attributes, we introduce a novel multi-task learning approach
that encourages feature competition among unrelated attributes. Then,
turning to the fine-grained attribute problem, we develop lazy local
approaches that generate prediction functions on the fly for each novel
test case. They make it possible to detect subtle differences between
very similar images, an essential capability for sophisticated image
search applications. Finally, we question the status quo of learning
purely object-independent attributes. Rather than train a single
classifier for each attribute, we explore a new form of large-scale
transfer that infers “analogous” class-sensitive attribute models. This
allows, for example, predicting what spottedness will look like on a
dog, when during training we have only observed spottedness on a
disjoint set of objects. We demonstrate our idea to learn over 25,000
object-sensitive attributes for SUN and ImageNet.
This is work with Dinesh Jayaraman, Aron Yu, and Chao-Yeh Chen.
+44 (0)20 7679 1176