cicyt UNIZAR
Full-text links:

Download:

Current browse context:

cs.CV

Change to browse by:

cs

References & Citations

DBLP - CS Bibliography

Bookmark

(what is this?)
CiteULike logo BibSonomy logo Mendeley logo del.icio.us logo Digg logo Reddit logo ScienceWISE logo

Computer Science > Computer Vision and Pattern Recognition

Title: Learning Unsupervised Visual Grounding Through Semantic Self-Supervision

Abstract: Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities. In the unsupervised setting, lack of supervisory signals exacerbate this difficulty. In this paper, we propose a novel framework for unsupervised visual grounding which uses concept learning as a proxy task to obtain self-supervision. The simple intuition behind this idea is to encourage the model to localize to regions which can explain some semantic property in the data, in our case, the property being the presence of a concept in a set of images. We present thorough quantitative and qualitative experiments to demonstrate the efficacy of our approach and show a 5.6% improvement over the current state of the art on Visual Genome dataset, a 5.8% improvement on the ReferItGame dataset and comparable to state-of-art performance on the Flickr30k dataset.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1803.06506 [cs.CV]
  (or arXiv:1803.06506v1 [cs.CV] for this version)

Submission history

From: Syed Ashar Javed [view email]
[v1] Sat, 17 Mar 2018 13:46:59 GMT (7253kb,D)