Text-to-image prompt refinement (T2I-Refine) aims to rephrase or extend an input prompt with more descriptive details that can be leveraged to generate images with higher quality. In this paper, we study an adversarial prompt attacking problem for T2I-Refine, where to goal is to implicitly inject specific concept bias to the input prompts during the refinement process so that the generated images, still with higher quality, are explicitly biased to the target group. Our study is motivated by the limitation of current T2I-Refine research that lacks of explorations on the potential capacity of T2I-Refine models to provide prompt refinement service in a biased or advertising manner. To address the limitations, we develop RAt, a prompt refinement and attacking framework that attacks input prompts with intentionally selected adversarial replacements by optimizing a token distribution matrix based on the text-to-image finetuning strategy with a token-level bias obfuscation loss as regularization. We evaluate RAt on a large-scale text-to-image dataset with various concepts as target in both in-domain and transfer-domain scenarios. The evaluation results demonstrate that, compared to other T2I-Refine schemes, RAt is well capable of implicitly attacking input prompts to generate images with higher quality and explicit visual bias towards specific concept group.

Question answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challenges for QA systems, especially when QA systems are trained in a source domain but deployed in a different target domain. In this work, we investigate the potential benefits of question classification for QA domain adaptation. We propose a novel framework: Question Classification for Question Answering (QC4QA). Specifically, a question classifier is adopted to assign question classes to both the source and target data. Then, we perform joint training in a self-supervised fashion via pseudo-labeling. For optimization, inter-domain discrepancy between the source and target domain is reduced via maximum mean discrepancy (MMD) distance. We additionally minimize intra-class discrepancy among QA samples of the same question class for fine-grained adaptation performance. To the best of our knowledge, this is the first work in QA domain adaptation to leverage question classification with self-supervised adaptation. We demonstrate the effectiveness of the proposed QC4QA with consistent improvements against the state-of-the-art baselines on multiple datasets.