Abstract
In this paper, we introduce the task of automatically generating text to describe the differences between two similar images. We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. Annotators were asked to succinctly describe all the differences in a short paragraph. As a result, our novel dataset provides an opportunity to explore models that align language and vision, and capture visual salience. The dataset may also be a useful benchmark for coherent multi-sentence generation. We perform a first-pass visual analysis that exposes clusters of differing pixels as a proxy for object-level differences. We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. We find that, for both single-sentence generation and as well as multi-sentence generation, the proposed model outperforms the models that use attention alone.- Anthology ID:
- D18-1436
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4024–4034
- Language:
- URL:
- https://aclanthology.org/D18-1436
- DOI:
- 10.18653/v1/D18-1436
- Cite (ACL):
- Harsh Jhamtani and Taylor Berg-Kirkpatrick. 2018. Learning to Describe Differences Between Pairs of Similar Images. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4024–4034, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Describe Differences Between Pairs of Similar Images (Jhamtani & Berg-Kirkpatrick, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1436.pdf
- Code
- harsh19/spot-the-diff
- Data
- Spot-the-diff