As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown…) attracting most attention. By building on the recent “zero-shot learning” approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as “visual phrases”, and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it out-performs various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.

CzEng 0.9 is the third release of a large parallel corpus of Czech and English. For the current release, CzEng was extended by significant amount of texts from various types of sources, including parallel web pages, electronically available books and subtitles. This paper describes and evaluates filtering techniques employed in the process in order to avoid misaligned or otherwise damaged parallel sentences in the collection. We estimate the precision and recall of two sets of filters. The first set was used to process the data before their inclusion into CzEng. The filters from the second set were newly created to improve the filtering process for future releases of CzEng. Given the overall amount and variance of sources of the data, our experiments illustrate the utility of parallel data sources with respect to extractable parallel segments. As a similar behaviour can be expected for other language pairs, our results can be interpreted as guidelines indicating which sources should other researchers exploit first.