Modeling semantic concepts in an embedding space as distributions is described. In the embedding space, both images and text labels are represented. The text labels describe semantic concepts that are exhibited in image content. In the embedding space, the semantic concepts described by the text labels are modeled as distributions. By using distributions, each semantic concept is modeled as a continuous cluster which can overlap other clusters that model other semantic concepts. For example, a distribution for the semantic concept “apple” can overlap distributions for the semantic concepts “fruit” and “tree” since can refer to both a fruit and a tree. In contrast to using distributions, conventionally configured visual-semantic embedding spaces represent a semantic concept as a single point. Thus, unlike these conventionally configured embedding spaces, the embedding spaces described herein are generated to model semantic concepts as distributions, such as Gaussian distributions, Gaussian mixtures, and so on.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method implemented by a computing device to annotate images with determined text labels to describe content of the images, the method comprising: generating an embedding space representing both images and text labels of a text vocabulary, said generating including: computing distributions to represent semantic concepts in the embedding space, the semantic concepts for which the distributions are computed being described by respective text labels of the text vocabulary and capable of being depicted in image content, and mapping representative images to the distributions of the embedding space, wherein the image content depicted by the representative images exemplifies corresponding semantic concepts of the distributions, determining, using the embedding space, at least one of the text labels describing at least one depicted semantic concept in the image content of an input image; and annotating the input image by associating the determined text labels with the input image. 2. A method as described in claim 1, wherein the distributions are computed as Gaussian distributions to represent the semantic concepts. 3. A method as described in claim 1, wherein the distributions are computed as Gaussian mixtures to represent the semantic concepts. 4. A method as described in claim 1, wherein generating the embedding space further includes: determining semantic relationships between the text labels of the text vocabulary; positioning the distributions in the embedding space based on the semantic relationships determined for the respective text labels; processing a plurality of training images, each training image having multiple text labels, said processing including generating sets of image regions that correspond to the respective multiple text labels; and setting the sets of image regions as the representative images for the mapping to the distributions of the embedding space. 5. A method as described in claim 4, wherein processing the plurality of training images includes, for each training image: determining candidate image regions for a respective set of image regions of the training image; and reducing a number of the determined candidate image regions using at least one post-processing technique. 6. A method as described in claim 5, wherein the candidate image regions are determined using geodesic object proposal. 7. A method as described in claim 5, wherein the at least one post-processing technique involves enforcing a size criterion by discarding candidate image regions having less than a threshold size. 8. A method as described in claim 5, wherein the at least one post-processing technique involves enforcing an aspect ratio criterion by discarding candidate image regions having aspect ratios outside predefined allowable aspect ratios. 9. A method as described in claim 5, wherein the at least one post-processing technique includes assigning a single candidate image region to each of the respective multiple text labels of the training image based on a single-label embedding model. 10. A method as described in claim 1, wherein determining the at least one text label describing the at least one depicted semantic concept in the image content of the input image includes: determining a set of semantically meaningful image regions of the input image; discarding at least one of the semantically meaningful image regions of the input image using a post-processing technique; and mapping remaining semantically meaningful image regions of the input image to the distributions of the embedding space to determine the at least one text label. 11. A method as described in claim 1, wherein determining the at least one text label includes computing distances in the embedding space between embeddings of semantically meaningful image regions of the input image and the distributions. 12. A method as described in claim 11, wherein the distances are computed using vectors that represent respective semantically meaningful image regions of the input image, the vectors extracted from the semantically meaningful image regions of the input image with a Convolutional Neural Network (CNN). 13. A method as described in claim 11, further comprising selecting the at least one text label for association with the input image based on the distances. 14. A method as described in claim 1, further comprising presenting indications of image regions of the input image that correspond to the at least one text label. 15. A system to annotate images with determined text labels to describe content of the images, the system comprising: one or more processors; and computer-readable storage media having stored thereon instructions that are executable by the one or more processors to perform operations comprising: processing a training image having multiple text labels, said processing including generating a set of image regions that correspond to the respective multiple text labels; embedding the set of image regions within an embedding space that represents semantic concepts as distributions, the semantic concepts represented being described by text labels of a text vocabulary and capable of being depicted in image content, the set of image regions embedded with the distributions that represent the semantic concepts depicted in the image content of the set of image regions; determining the text labels that describe depicted semantic concepts of a query image by mapping image regions of the query image to the distributions of the embedding space; and annotating the query image with at least two of the determined text labels. 16. A system as described in claim 15, further comprising semantically relating the text labels of the text vocabulary before embedding the image regions of a plurality of training images in the embedding space. 17. A system as described in claim 16, wherein the text labels of the text vocabulary are semantically related based on the Glove model. 18. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by the computing device, perform operations comprising: maintaining an image annotated with at least one text label describing at least one respective semantic concept exhibited by image content of the image, the at least one text label discovered for the image using an embedding space that represents semantic concepts as distributions rather than representing the semantic concepts as single points, the semantic concepts represented being described by the text labels of a text vocabulary and capable of being exhibited by the image content. 19. One or more computer-readable storage media as described in claim 18, wherein the image is annotated with the at least one text label as part of indexing the image for search. 20. One or more computer-readable storage media as described in claim 18, wherein the distributions that represent the semantic concepts are at least one of Gaussian distributions or Gaussian mixtures.