Ensuring consistent persona in interactive AI systems presents a significant challenge, especially in diverse application scenarios ranging from virtual assistants to customer service bots. Such capability is often constrained by the system's understanding of direct and explicit persona conflicts. Traditional approaches primarily focus on detecting discrepancies between machine responses and its predefined profile, or the contextual inconsistencies between the responses at the semantic level rather than the persona level. Due to the lack of a comprehensive persona-specific Commonsense Knowledge Graph, some indirect and implicit persona inconsistencies between machine responses can hardly be identified. In this paper, we build the first persona commonsense knowledge graph (PersonaKG), based on which we then construct a large-scale persona consistency dialogue dataset (PersonaCOM) containing both explicit and implicit persona conflicts between machine responses. With the guidance of the persona commonsense knowledge, we propose a Recognize-Rewrite framework (R2) which first recognizes the responses that are inconsistent in persona with the previous responses, and then rewrites them into consistent ones. The empirical study demonstrates that utilizing R2 method on PersonaCOM with PersonaKG results in a significant improvement of 12.20% in automatic metrics and 10.09% in manual evaluation compared to not using the R2 method and PersonaKG.

Detecting small objects (e.g., those smaller than 20 × 20 pixels) in large-scale images remains a significant and challenging problem. Modern CNN-based detectors often struggle due to the scale mismatch between pre-training datasets and the target dataset used for detector fine-tuning. In this paper, we investigate the impact of scale alignment between pre-training and target datasets, and introduce an effective Object scale alignmenting (OSA) approach to align object scales across datasets, thereby enhancing the representation of small objects. Furthermore, we propose a refined version of the OSA method, termed OSA-RF, which improves scale alignment from the image level to the instance level, strengthening the similarity between pre-training and target datasets. To address the potential distortion of image structure caused by traditional scale alignmenting, we introduce a novel Probabilistic Structure Inpainting (PSI) method for background processing. Extensive experiments across multiple detectors demonstrate that OSA-RF significantly improves performance on the TinyPerson dataset and outperforms state-of-the-art detectors by a considerable margin.