Humans connect language and vision to perceive the world. How to build a similar connection for computers? One possible way is via visual concepts, which are text terms that relate to visually discriminative entities. We propose an automatic visual concept discovery algorithm using parallel text and visual corpora; it filters text terms based on the visual discriminative power of the associated images, and groups them into concepts using visual and semantic similarities. We illustrate the applications of the discovered concepts using bidirectional image and sentence retrieval task and image tagging task, and show that the discovered concepts not only outperform several large sets of manually selected concepts significantly, but also achieves the stateof-the-art performance in the retrieval task. * This work was done when Chuang Gan was a visiting researcher at University of Southern California.
REF applies extensive NLP parsing techniques (e.g., unigram terms and grammatical relations) to extract concepts that are consequently filtered by the discriminative power of visual cues and grouped by joint visual and semantic similarities.
Automatic Concept Discovery from Parallel Text and Visual Corpora
2015 IEEE International Conference on Computer Vision (ICCV)
2015 IEEE International Conference on Computer Vision (ICCV)
Computer Science