We chose the Google Search corpus [Luo et al., 2021] for our question-answering system as it provides good coverage of the knowledge needed and is publicly available. However, as noted by the authors of RA-VQA, additional knowledge bases may be required to answer some questions correctly. Future work may address the issue by improving the quality and expanding the coverage of knowledge. We do not perceive any immediate ethical concerns associated with the misuse of our proposed system. There is a possibility that the trained KB-VQA system might generate inappropriate or biased content as a result of the training data biases during LLM and LMM pre-training and fine-tuning.

Knowledge-based Visual Question Answering (KB-VQA) requires VQA systems to utilize knowledge from external knowledge bases to answer visually-grounded questions. Retrieval-Augmented Visual Question Answering (RA-VQA), a strong framework to tackle KB-VQA, first retrieves related documents with Dense Passage Retrieval (DPR) and then uses them to answer questions. This paper proposes Fine-grained Late-interaction Multi-modal Retrieval (FLMR) which significantly improves knowledge retrieval in RA-VQA. FLMR addresses two major limitations in RA-VQA's retriever: (1) the image representations obtained via image-to-text transforms can be incomplete and inaccurate and (2) relevance scores between queries and documents are computed with one-dimensional embeddings, which can be insensitive to finer-grained relevance. FLMR overcomes these limitations by obtaining image representations that complement those from the image-totext transforms using a vision model aligned with an existing text-based retriever through a simple alignment network. FLMR also encodes images and questions using multi-dimensional embeddings to capture finer-grained relevance between queries and documents. FLMR significantly improves the original RA-VQA retriever's PRRecall@5 by approximately 8%. Finally, we equipped RA-VQA with two state-of-the-art large multi-modal/language models to achieve 61% VQA score in the OK-VQA dataset.

Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-toevaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimization domain. However, these costs can be highly heterogeneous and are often unknown in advance. This occurs in many practical settings, such as hyperparameter tuning of machine learning algorithms or physics-based simulation optimization. Moreover, those few existing methods that acknowledge cost heterogeneity do not naturally accommodate a budget constraint on the total evaluation cost.

World's longest tiramisu record broken in London The record for the world's longest tiramisu has been broken in London. One-hundred Italian chefs gathered at Chelsea Town Hall on Saturday and Sunday to whip up a tiramisu long enough to topple the previous record set by Milanese Galbani in Milan, which stood at 273.5m (897ft). As per Guinness World Record rules, the record-breaking tiramisu was made and assembled live on site, and used 50,000 ladyfinger biscuits and more than 3,000 eggs. The record was broken with the dessert measuring 440.6 m (1,445ft). Mirko Ricci, the man behind the London record attempt, originally held the record in 2017 in Italy, but another Italian team broke that in 2019.

The hoard was likely buried sometime between 871 and 874 in present-day Worcestershire. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. One of the silver coins in the hands of their finder, moments after being found. Breakthroughs, discoveries, and DIY tips sent six days a week. In a classic example of lucky metal detectorists triggering an archaeological investigation, a group of metal detecting enthusiasts in England discovered a rare hoard of early medieval Anglo-Saxon coins in the parish of Bickmarsh, Worcestershire.

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Kenyan runner Sabastian Sawe set a new men's marathon world record at the London Marathon, finishing in 1 hour, 59 minutes and 30 seconds on Sunday.