We present the participation of the MemoriEase lifelog retrieval system in the NTCIR-18 Lifelog 6 Task. This current MemoriEase system is an automatic and enhanced version of the MemoriEase system at the Lifelog Search Challenge 2024 (LSC). We report our methods for the two core sub-tasks in the NTCIR-18 Lifelog 6 task, Lifelog Semantic Access (LSAT) and Lifelog Question Answer (LQAT). We enhance the main architecture of the MemoriEase system utilizing the BLIP2 and CLIP embedding models to extract visual embedding and perform a comparison between the two models. In addition, we also use pseudo-relevance feedback for ad-hoc queries. For the LQAT sub-task, we use our retrieval model as the retriever and GPT-4o as a reader to generate answers to questions. Results of the LSAT sub-task show that our system found 369 images in 1,995 relevant images. The performance on known-item search queries is higher than on Ad-hoc queries, with 28.22% R@ 5 compared to 5.98% R@ 5, respectively. In the LQAT sub-task, the LLM model generates 8 correct answers in 24 questions. Although the performance is not high, it shows the advantages and drawbacks of the MemoriEase retrieval system and the QA model.