9 research outputs found
RecipeMeta: Metapath-enhanced Recipe Recommendation on Heterogeneous Recipe Network
Recipe is a set of instructions that describes how to make food. It can help
people from the preparation of ingredients, food cooking process, etc. to
prepare the food, and increasingly in demand on the Web. To help users find the
vast amount of recipes on the Web, we address the task of recipe
recommendation. Due to multiple data types and relationships in a recipe, we
can treat it as a heterogeneous network to describe its information more
accurately. To effectively utilize the heterogeneous network, metapath was
proposed to describe the higher-level semantic information between two entities
by defining a compound path from peer entities. Therefore, we propose a
metapath-enhanced recipe recommendation framework, RecipeMeta, that combines
GNN (Graph Neural Network)-based representation learning and specific
metapath-based information in a recipe to predict User-Recipe pairs for
recommendation. Through extensive experiments, we demonstrate that the proposed
model, RecipeMeta, outperforms state-of-the-art methods for recipe
recommendation
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
Small Object Detection (SOD) is an important machine vision topic because (i)
a variety of real-world applications require object detection for distant
objects and (ii) SOD is a challenging task due to the noisy, blurred, and
less-informative image appearances of small objects. This paper proposes a new
SOD dataset consisting of 39,070 images including 137,121 bird instances, which
is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The
detail of the challenge with the SOD4SB dataset is introduced in this paper. In
total, 223 participants joined this challenge. This paper briefly introduces
the award-winning methods. The dataset, the baseline code, and the website for
evaluation on the public testset are publicly available.Comment: This paper is included in the proceedings of the 18th International
Conference on Machine Vision Applications (MVA2023). It will be officially
published at a later date. Project page :
https://www.mva-org.jp/mva2023/challeng
HOYO:オノマトペを付与した歩容データセット
HOYOは多様なオノマトペが付与された歩容のデータセットです.各シーンは自由記述オノマトペ,10種選択式オノマトペ,歩行者の主観オノマトペが付与されています.擬態語の研究で使われています.We have built and released a video dataset where gaits are expressed by various onomatopoeias according to their appearance. Each gait is annotated both by external judgement as well as the actors own judgment. This dataset is used in our project on mimetic words