Masked Image Modeling (MIM) has been widely recognized as a powerful self-supervised paradigm for learning general-purpose visual representations. However, standard MIM based on random masking tends to underperform in domain-specific tasks like Scene Text Recognition (STR), due to challenges such as information sparsity and appearance discrepancies caused by partial occlusion or distortion. To address this issue, we propose a novel pre-training framework called Appearance Discrepancy-guided Sequence Hybrid Masking (DSHM), specifically designed to learn robust representations for STR. To this end, we introduce an Appearance Discrepancy Metric that quantifies the discrepancy level of each image patch by measuring its deviation from anisotropic local discrepancy and intra-instance global style discrepancy. The resulting discrepancy scores are utilized in two key components: (1) A Sequence Hybrid Masking strategy, which prioritizes masking high-discrepancy patches in coherent block forms, thereby elevating the pretext task from simple pixel-level completion to more complex structural reasoning; (2) Discrepancy-Conditioned Tokens (DC-Tokens), which encode prior knowledge about patch difficulty into the decoder, enabling an adaptive reconstruction process and improving the model robustness under scenarios with partial occlusion or text distortion. We achieve competitive performance on multiple benchmark datasets, including common benchmarks, Union14M benchmarks, and Chinese benchmarks.

Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource scenarios, where the available training data is scarce, previous prompt-based methods may still perform poorly for prompt-based representation learning due to a superficial understanding of the relation. To this end, we highlight the importance of learning high-quality relation representation in low-resource scenarios for RE, and propose a novel prompt-based relation representation method, named MVRE (Multi-View Relation Extraction), to better leverage the capacity of PLMs to improve the performance of RE within the low-resource prompt-tuning paradigm. Specifically, MVRE decouples each relation into different perspectives to encompass multi-view relation representations for maximizing the likelihood during relation inference. Furthermore, we also design a Global-Local loss and a Dynamic-Initialization method for better alignment of the multi-view relation-representing virtual words, containing the semantics of relation labels during the optimization learning process and initialization. Extensive experiments on three benchmark datasets show that our method can achieve state-of-the-art in low-resource settings.

Recently, neural module networks (NMNs) have yielded ongoing success in answering compositional visual questions, especially those involving multi-hop visual and logical reasoning. NMNs decompose the complex question into several sub-tasks using instance-modules from the reasoning paths of that question and then exploit intermediate supervisions to guide answer prediction, thereby improving inference interpretability. However, their performance may be hindered due to sketchy modeling of intermediate supervisions. For instance, (1) a prior assumption that each instance-module refers to only one grounded object yet overlooks other potentially associated grounded objects, impeding full cross-modal alignment learning; (2) IoU-based intermediate supervisions may introduce noise signals as the bounding box overlap issue might guide the model's focus towards irrelevant objects. To address these issues, a novel method, Detection-based Intermediate Supervision (DIS), is proposed, which adopts a generative detection framework to facilitate multiple grounding supervisions via sequence generation. As such, DIS offers more comprehensive and accurate intermediate supervisions, thereby boosting answer prediction performance. Furthermore, by considering intermediate results, DIS enhances the consistency in answering compositional questions and their sub-questions. Extensive experiments demonstrate the superiority of our proposed DIS, showcasing both improved accuracy and state-of-the-art reasoning consistency compared to prior approaches.

Temporal Knowledge Graph (TKG) is an extension of regular knowledge graph by attaching the time scope. Existing temporal knowledge graph question answering (TKGQA) models solely approach simple questions, owing to the prior assumption that each question only contains a single temporal fact with explicit/implicit temporal constraints. Hence, they perform poorly on questions which own multiple temporal facts. In this paper, we propose Joint Multi Facts Reasoning Network (JMFRN), to jointly reasoning multiple temporal facts for accurately answering complex temporal questions. Specifically, JMFRN first retrieves question-related temporal facts from TKG for each entity of the given complex question. For joint reasoning, we design two different attention (i.e., entity-aware and time-aware) modules, which are suitable for universal settings, to aggregate entities and timestamps information of retrieved facts. Moreover, to filter incorrect type answers, we introduce an additional answer type discrimination task. Extensive experiments demonstrate our proposed method significantly outperforms the state-of-art on the wellknown complex temporal question benchmark TimeQuestions.

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations.Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts.However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge.To address this, we propose a novel Treestructure Reasoning schEmA named TREA.TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results.Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.Our