Multimodal Relation Extraction (MRE) is a core task for constructing Multimodal Knowledge images (MKGs). Most current research is based on fine-tuning small-scale single-modal image and text pre-trained models, but we find that image-text datasets from network media suffer from data scarcity, simple text data, and abstract image information, which requires a lot of external knowledge for supplementation and reasoning. We use Multimodal Relation Data augmentation (MRDA) to address the data scarcity problem in MRE, and propose a Flexible Threshold Loss (FTL) to handle the imbalanced entity pair distribution and long-tailed classes. After obtaining prompt information from the small model as a guide model, we employ a Large Language Model (LLM) as a knowledge engine to acquire common sense and reasoning abilities. Notably, both stages of our framework are flexibly replaceable, with the first stage adapting to multimodal related classification tasks for small models, and the second stage replaceable by more powerful LLMs. Through experiments, our EMRE2llm model framework achieves state-of-the-art performance on the challenging MNRE dataset, reaching an 82.95% F1 score on the test set.

The extensive use of large language models (LLMs) across various natural language processing tasks has markedly elevated the intelligence of legal systems. Despite their exceptional performance in terms of accuracy, these systems still struggle with explainability. To tackle this challenge, we propose an approach to boost the question-answering abilities of LLMs through data synthesis, focusing on Qwen-7B. By incorporating Retrieval-Augmented Generation (RAG) techniques, we enhance the system’s transparency and reliability by introducing detailed reasoning processes (CoT Prompts). Our experimental results indicate that our trained LLMs exhibit significant improvements in both answer accuracy and explainability, especially in objective evaluation tasks. Additionally, subjective assessments reveal that the model’s responses are not only precise but also highly understandable, thus boosting user confidence in the system. Overall, our research offers valuable insights and technical advancements for the development of intelligent legal question-answering systems, with significant theoretical and practical implications.

In the field of human pose estimation, heatmap-based methods have emerged as the dominant approach, and numerous studies have achieved remarkable performance based on this technique. However, the inherent drawbacks of heatmaps lead to serious performance degradation in methods based on heatmaps for smaller-scale persons. While some researchers have attempted to tackle this issue by improving the performance of small-scale persons, their efforts have been hampered by the continued reliance on heatmap-based methods. To address this issue, this paper proposes the SSA Net, which aims to enhance the detection accuracy of small-scale persons as much as possible while maintaining a balanced perception of persons at other scales. SSA Net utilizes HRNetW48 as a feature extractor and leverages the TDAA module to enhance small-scale perception. Furthermore, it abandons heatmap-based methods and instead adopts coordinate vector regression to represent keypoints. Notably, SSA Net achieved an AP of 77.4% on the COCO Validation dataset, which is superior to other heatmap-based methods. Additionally, it achieved highly competitive results on the Tiny Validation and MPII datasets as well.

Legal case retrieval requires identifying relevant cases from lengthy judicial documents based on factual and legal similarities. Existing methods often match cases through global semantic representations. When candidates are semantically similar, global representations may underrepresent discriminative factual details and judicial reasoning. We propose CECRA, a structured evidence-aware reranking method that uses Qwen3-32B to construct Summary, Fact Evidence, and Reasoning Evidence offline. CECRA combines global Summary matching with fixed, charge-independent Fact Evidence and Reasoning Evidence via validity-masked slot-level local interaction; Agreement remains a secondary matching component with auxiliary supervision. On LeCaRDv2 under the fixed-candidate-pool evaluation protocol, mean average precision (MAP), averaged over three seeds, was 72.95 for CECRA, 70.72 for fine-tuned SAILER (SAILER-FT), and 67.32 for KELLER. Jointly removing both local views and associated Agreement calculations reduced MAP by 2.24 percentage points, consistent with a contribution from structured local evidence but not isolating Agreement’s independent effect. Pairwise diagnostics showed the largest gains for candidates with similar global semantics but different local evidence. These results indicate that local structured evidence can complement case-level semantic representations in distinguishing highly similar candidates.

This paper introduces LocRecNet, a deformation-aware network for table localization and correction, aimed at improving the recognition accuracy of complex table data. Conventional algorithms typically depend on table cell or line features for model training but exhibit limitations when processing real-world deformed table data. LocRecNet addresses these challenges by correcting deformations prior to table structure recognition, significantly enhancing model performance. The proposed network employs a novel keypoint detection method to precisely locate table edge points, enabling the efficient correction of deformed tables. Experimental results reveal that integrating LocRecNet substantially improves table recognition algorithms in terms of various key performance metrics, with recall rates increasing by up to 10% and F1 scores nearing 90%. Tests conducted on real-world datasets further validate its effectiveness, demonstrating a reasonable trade-off between computational cost and performance gains. Additionally, LocRecNet enhances performance even on standard table data, highlighting its strong generalizability and potential for broader application.