Nuvi Labs Co., Ltd.

Jey Yoon Ru

Dae Hoon Kim

Luca Pimenta Medeiros

Seung Woo Ji

Provided are a method and an apparatus for identifying food using a multimodal model. A method for identifying food using a multimodal model according to one embodiment of the present disclosure comprises setting a plurality of food images belonging to different classes as pre-learning targets; pre-learning a multimodal model for each of the plurality of food images set as the pre-learning target so that a text feature paired with an image feature is more similar to the image feature than the other text features not paired with the image feature; and identifying food to be identified from input food images using the pre-learned multimodal model.

Method and apparatus for identifying food using multimodal model

Provided are a method and an apparatus for auto segmentation using a bounding box. A method for auto segmentation using a bounding box according to one embodiment of the present disclosure comprises receiving a first object image including an object labeled with a bounding box, which is a pre-learning target, learning a segmentation model by classifying an object and a background from the bounding box of the received first object image, and segmenting an object from a second object image, which is an identification target, using the learned segmentation model.

Methods and apparatuses for auto segmentation using bounding box

Provided are a method and an apparatus for analyzing food using image captioning. A method for analyzing food using image captioning according to one embodiment of the present disclosure comprises generating image captioning data using food image features extracted from a food image; and generating a food name for the food image using the generated image captioning data.

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Seoul, South Korea

Jey Yoon Ru