SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning

• URL: http://arxiv.org/abs/2009.09566v2
• Date: Tue, 29 Sep 2020 00:24:25 GMT
• Title: SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning
• Authors: Tsu-Jui Fu, Xin Eric Wang, Scott Grafton, Miguel Eckstein, William Yang Wang
• Abstract summary: Iterative Language-Based Image Editing (IL-BIE) tasks follow iterative instructions to edit images step by step. Data scarcity is a significant issue for ILBIE as it is challenging to collect large-scale examples of images before and after instruction-based changes. We introduce a Self-Supervised Counterfactual Reasoning framework that incorporates counterfactual thinking to overcome data scarcity.
• Score: 79.30956389694184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Iterative Language-Based Image Editing (IL-BIE) tasks follow iterative instructions to edit images step by step. Data scarcity is a significant issue for ILBIE as it is challenging to collect large-scale examples of images before and after instruction-based changes. However, humans still accomplish these editing tasks even when presented with an unfamiliar image-instruction pair. Such ability results from counterfactual thinking and the ability to think about alternatives to events that have happened already. In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity. SSCR allows the model to consider out-of-distribution instructions paired with previous images. With the help of cross-task consistency (CTC), we train these counterfactual instructions in a self-supervised scenario. Extensive results show that SSCR improves the correctness of ILBIE in terms of both object identity and position, establishing a new state of the art (SOTA) on two IBLIE datasets (i-CLEVR and CoDraw). Even with only 50% of the training data, SSCR achieves a comparable result to using complete data.

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