Modularized Textual Grounding for Counterfactual Resilience

Zhiyuan Fang, Shu Kong, Charless Fowlkes, Yezhou Yang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6378-6388

Abstract


Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries. To achieve high grounding precision, current textual grounding methods heavily rely on large-scale training data with manual annotations at the pixel level. Such annotations are expensive to obtain and thus severely narrow the model's scope of real-world applications. Moreover, most of these methods sacrifice interpretability, generalizability, and they neglect the importance of being resilient to counterfactual inputs. To address these issues, we propose a visual grounding system which is 1) end-to-end trainable in a weakly supervised fashion with only image-level annotations, and 2) counterfactually resilient owing to the modular design. Specifically, we decompose textual descriptions into three levels: entity, semantic attribute, color information, and perform compositional grounding progressively. We validate our model through a series of experiments and demonstrate its improvement over the state-of-the-art methods. In particular, our model's performance not only surpasses other weakly/un-supervised methods and even approaches the strongly supervised ones, but also is interpretable for decision making and performs much better in face of counterfactual classes than all the others.

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[bibtex]
@InProceedings{Fang_2019_CVPR,
author = {Fang, Zhiyuan and Kong, Shu and Fowlkes, Charless and Yang, Yezhou},
title = {Modularized Textual Grounding for Counterfactual Resilience},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}