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.

Transformer has a wide range of applications in human posture estimation. It can model the global dependence relationship of images through the self-attention mechanism to obtain key human body information. However, Transformer consumes a lot of computation. An axial compression pose transformer (ACPose) method is proposed to reduce part of the computational cost of Transformer by the axial compression of the input matrix, while maintaining the global receptive field by feature fusion. A Local Enhancement Module is constructed to avoid the loss of too much feature information in the compression process. In the COCO dataset experiment, there was a significant reduction in computational cost compared to those of state-of-the-art transformer-based algorithms.

Scale variation is a major challenge in multi-person pose estimation. In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations. Therefore, effectively balancing the persons of different scales poses a significant challenge. So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model’s performance in complex scenes. Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage’s four branches into the DCB module. The dilated convolution-based (DCB) module employs a parallel structure that incorporates dilated convolutions with different rates to expand the receptive field of each branch. Subsequently, the attention operation-based (AOB) module performs attention operations at both branch and channel levels to enhance high-frequency features and reduce the influence of noise. Finally, predictions are made using the heatmap representation. The model can recognize images with diverse scales and more complex semantic information. Experimental results demonstrate that FSA Net achieves competitive results on the MSCOCO and MPII datasets, validating the effectiveness of our proposed approach.