Titlebook: ;

relieve

CAMP

CafeLLM: Context-Aware Fine-Grained Semantic Clustering Using Large Language Modelsed and esoteric in many domains, presenting unique challenges that conventional named entity recognition (NER) or clustering methods fail to address. Here, we present CafeLLM, a Context-Aware Fine-grained clustering method that uses Large Language Models (LLMs) to cluster terms or phrases from these

思想

Vaginismus

MADP: Multi-modal Sequence Learning for?Alzheimer’s Disease Prediction with?Missing Dataease progression and improving the quality of life for affected individuals. A significant challenge in this context is the substantial amount of missing data, which arises due to the variable health status of subjects or other unpredictable circumstances. Moreover, existing methods struggle to accu

JUST

gusher

Improved VLN-BERT with?Reinforcing Endpoint Alignment for?Vision-and-Language Navigationng visual information from the surroundings. Currently, many pre-trained models and pre-training tasks have been proposed to assist agents in navigating unfamiliar environments using visual and linguistic information. However, ensuring that the agent stops near the endpoint is a challenging problem.

CAB

Bridging the Language Gap: Domain-Specific Dataset Construction for Medical LLMss a variety of tasks such as text generation, translation, and question answering. However, their effectiveness in specialized domains is constrained by the lack of domain-specific data. This paper presents an effective methodology for constructing domain-specific datasets using domain-specific corp

我就不公正

ticlopidine

Semantic-Degrade Learning Framework for?Open World Object Detectionnature of real-world scenarios where systems encounter unknown objects. Unlike existing OWOD approaches which often rely on manually selected unknown proposals, we introduce an Adaptive Semantic-Degrade Learning framework. This framework, inspired by cognitive development theory, guides the model to

結(jié)果

Multi-modal Prompts with?Feature Decoupling for?Open-Vocabulary Object Detectionor training. The Prompt serves as a template to assist in the construction of textual descriptions for categories. With the development of open-vocabulary object detection, multi-modal prompts with better performance have emerged. However, existing multi-modal prompts fail to align the context and o