CV/NLP

NLP

In the field of natural language processing, with the continuous development of artificial intelligence technology, how to enable machines to understand and generate more profound and semantic language has become one of the core research issues. Especially traditional rule-based and template based language processing methods are gradually unable to meet complex language requirements, especially in diverse and unstructured language environments. For example, semantic understanding and contextual reasoning often become bottlenecks in system accuracy and performance.

To explore this field, our research proposes several innovative solutions. Intent networks: Capturing the potential needs and intentions of users through in-depth analysis of their inputs, thereby providing accurate semantic guidance for subsequent tasks. Code generation: Automatically generating code based on understanding complex semantic structures and task descriptions. LLM Retrieval: Accurately extract information related to specific problems from massive amounts of data, achieving efficient semantic matching and information retrieval using LLMs. Semantic LLM: Capturing subtle differences in language, understanding complex grammar and semantic structures, and demonstrating powerful capabilities in tasks such as contextual reasoning, sentiment analysis, and language generation.

CV

In the field of computer vision (CV), with the advancement of technology, how to effectively understand and analyze human movements, detect abnormal behaviors, and enhance the interactivity of video content has become one of the key issues in current research. Although traditional computer vision methods have made significant progress in static image analysis, there are still significant challenges in understanding and processing dynamic video content, especially in terms of accuracy for tasks such as human pose estimation, anomaly detection, and intelligent interaction. For example, Anomaly detection often requires processing high-dimensional and dynamically changing data, which places higher demands on traditional machine learning methods.

To address these challenges, our research focuses on several important areas: human pose estimation, VAD anomaly detection, intelligent video playback, intelligent digital humans, and innovative applications in the intersection of medical and engineering fields.