In this paper, we introduce OpenCIR, a fully-functional Conditional Image Repainting (CIR) model designed for local image editing. Given an image and a combination of conditions related to geometry, texture, and color, CIR models are required to repaint instances and seamlessly composite them with the original images. Previous CIR models suffer from limited object categories, restricted condition modalities, and demanded geometry precision. In contrast, leveraging the generative priors from pre-trained models, OpenCIR could repaint open object categories. Equipped with redesigned condition injection modules and the condition extension strategy, OpenCIR is able to understand open condition modalities. Adopting the contour refinement strategy, OpenCIR allows users to specify instances with open geometry precision. In addition, we contribute the Open-CIR dataset, which includes detailed annotations, tailored for the comprehensive training and evaluation of the OpenCIR model. Extensive experiments demonstrate that OpenCIR outperforms relevant state-of-the-art methods, achieving superior visual quality, and more favorable results by human evaluators.

Joint Multimodal Entity-Relation Extraction (JMERE) aims to extract entity-relationship triples in texts from given image-text pairs. As a joint multimodal information extraction task, it has attracted increasing research interest. Previous works of JMERE typically utilize graph networks to align textual entities and visual objects and achieve promising performance. However, these methods do not pay attention to the inconsistency between text and image and the straight alignment could limit the performance of JMERE models. In this paper, we propose a Consistency-adaptive text-image Alignment Generation (CAG) framework for various text-image consistency scenarios. Specifically, we propose a Consistency Factor (CF) to measure the consistency between images and texts. We also design consistency-adaptive contrastive learning based on CF, which can reduce the impact of inconsistent visual and textual information. Additionally, we adopt JMERE-specifical instruction tuning for better entity-relationship triplet generation. Experimental results on the JMERE dataset demonstrate that our proposed CAG is effective and achieves state-of-the-art performance.

Document-level event extraction (DEE) faces two main challenges: arguments-scattering and multi-event. Although previous methods attempt to address these challenges, they overlook the interference of event-unrelated sentences during event detection and neglect the mutual interference of different event roles during argument extraction. Therefore, this paper proposes a novel Schema-based Explicitly Aggregating (SEA) model to address these limitations. SEA aggregates event information into event type and role representations, enabling the decoding of event records based on specific type-aware representations. By detecting each event based on its event type representation, SEA mitigates the interference caused by event-unrelated information. Furthermore, SEA extracts arguments for each role based on its role-aware representations, reducing mutual interference between different roles. Experimental results on the ChFinAnn and DuEE-fin datasets show that SEA outperforms the SOTA methods.