74 research outputs found
Cost-Effective HITs for Relative Similarity Comparisons
Similarity comparisons of the form "Is object a more similar to b than to c?"
are useful for computer vision and machine learning applications.
Unfortunately, an embedding of points is specified by triplets,
making collecting every triplet an expensive task. In noticing this difficulty,
other researchers have investigated more intelligent triplet sampling
techniques, but they do not study their effectiveness or their potential
drawbacks. Although it is important to reduce the number of collected triplets,
it is also important to understand how best to display a triplet collection
task to a user. In this work we explore an alternative display for collecting
triplets and analyze the monetary cost and speed of the display. We propose
best practices for creating cost effective human intelligence tasks for
collecting triplets. We show that rather than changing the sampling algorithm,
simple changes to the crowdsourcing UI can lead to much higher quality
embeddings. We also provide a dataset as well as the labels collected from
crowd workers.Comment: 7 pages, 7 figure
Conditional Similarity Networks
What makes images similar? To measure the similarity between images, they are
typically embedded in a feature-vector space, in which their distance preserve
the relative dissimilarity. However, when learning such similarity embeddings
the simplifying assumption is commonly made that images are only compared to
one unique measure of similarity. A main reason for this is that contradicting
notions of similarities cannot be captured in a single space. To address this
shortcoming, we propose Conditional Similarity Networks (CSNs) that learn
embeddings differentiated into semantically distinct subspaces that capture the
different notions of similarities. CSNs jointly learn a disentangled embedding
where features for different similarities are encoded in separate dimensions as
well as masks that select and reweight relevant dimensions to induce a subspace
that encodes a specific similarity notion. We show that our approach learns
interpretable image representations with visually relevant semantic subspaces.
Further, when evaluating on triplet questions from multiple similarity notions
our model even outperforms the accuracy obtained by training individual
specialized networks for each notion separately.Comment: CVPR 201
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