# DAVIDSTUTZ

Meet me at CVPR'18: Tuesday, June 19th, I will be presenting our work on weakly-supervised 3D shape completion.
07thMAY2016

Chum et al. bring query expansion, as previously known from text retrieval, to the visual domain. The idea of query expansion is to improve query results by re-issuing a number of highly ranked results as new query to include further relevant results. Average Query Expansion uses the top $K^\ast$ of $K$ retrieved images and averages the corresponding term-frequency (i.e. Chum et al. use the Bag of Visual Words model with term-frequency weighting [1]) representation:
$z_\text{avg} = \frac{1}{K^\ast + 1} \left(z_0 + \sum_{k = 1}^{K^\ast} z_k\right)$
where $z_0$ is the query image and $z_1, \ldots, z_K$ are the retrieved images. The results of query $z_\text{avg}$ is appended to the first $K^\ast$ results of the original query. Usually, this type of query expansion is used in combination with spatial verification such that only verified results are included in the query expansion.