SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning
Tsu-Jui Fu, Xin Eric Wang, Scott T. Grafton, Miguel Patricio Eckstein, William Yang Wang · 2020
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-ofdistribution 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.