Referring expressions are natural language construc- tions used to identify particular objects within a scene. In this paper, we propose a unified framework for the tasks of referring expression comprehension and generation. Our model is composed of three modules: speaker, listener, and reinforcer. The speaker generates referring expressions, the listener comprehends referring expressions, and the rein- forcer introduces a reward function to guide sampling of more discriminative expressions. The listener-speaker mod- ules are trained jointly in an end-to-end learning frame- work, allowing the modules to be aware of one another during learning while also benefiting from the discrimina- tive reinforcer’s feedback. We demonstrate that this unified framework and training achieves state-of-the-art results for both comprehension and generation on three referring ex- pression datasets.
Understanding Synonymous Referring Expressions via Contrastive Features
Referring expression comprehension aims to localize objects identified by natural language descriptions. This is a challenging task as it requires understanding of both visual and language domains. One nature is that each object can be described by synonymous sentences with paraphrases, and such varieties in languages have critical impact on learning a comprehension model. While prior work usually treats each sentence and attends it to an object separately, we focus on learning a referring expression comprehension model that considers the property in synonymous sentences. To this end, we develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels, where features extracted from synonymous sentences to describe the same object should be closer to each other after mapping to the visual domain. We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets, and demonstrate that our method performs favorably against the state-of-the-art approaches. Furthermore, since the varieties in expressions become larger across datasets when they describe objects in different ways, we present the cross-dataset and transfer learning settings to validate the ability of our learned transferable features.
- Publication Date:
- NSF-PAR ID:
- 10370800
- Journal Name:
- International Journal of Computer Vision
- Volume:
- 130
- Issue:
- 10
- Page Range or eLocation-ID:
- p. 2501-2516
- ISSN:
- 0920-5691
- Publisher:
- Springer Science + Business Media
- Sponsoring Org:
- National Science Foundation
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