Korea Electronics Technology Institute

Ls-Nikko Copper Inc.

Bo Eun Kim

Hye Dong Jung

Choong Sang Cho

Young Han Lee

Sa Im Shin

Han Mu Park

Min Jae Lee

Sung Chen Ahan

Chung Il Kim

Chong Ho Sonu

Byoung Kook Jeon

The present invention relates to an image inpainting apparatus and an image inpainting method, the image inpainting apparatus including: a background inpainting part configured to generate a background-inpainted image by carrying out inpainting on a background with respect to an input image in which a region to be inpainted is set up; an object inpainting part configured to generate an object image by carrying out inpainting on an object; and an image overlapping part configured to generate an output image by causing the background-inpainted image and the object image, which are generated, to overlap each other.

Image inpainting apparatus and image inpainting method

A method and a system for automatically generating multiple captions of an image are provided. A method for training an auto image caption generation model according to an embodiment of the present disclosure includes: generating a caption attention map by using an image; converting the generated caption attention map into a latent variable by projecting the caption attention map onto a latent space; deriving a guide map by using the latent variable; and training to generate captions of an image by using the guide map and the image. Accordingly, a plurality of captions describing various characteristics of an image and including various expressions can be automatically generated.

Method and system for auto multiple image captioning

A method and a system for automatic image caption generation are provided. The automatic image caption generation method according to an embodiment of the present disclosure includes: extracting a distinctive attribute from example captions of a learning image; training a first neural network for predicting a distinctive attribute from an image, by using a pair of the extracted distinctive attribute and the learning image; inferring a distinctive attribute by inputting the learning image to the trained first neural network; and training a second neural network for generating a caption of an image by using a pair of the inferred distinctive attribute and the learning image. Accordingly, a caption well indicating a feature of a given image is automatically generated, such that an image can be more exactly explained and a difference from other images can be clearly distinguished.

Method and system for automatic image caption generation

Valuable metals such as cobalt, nickel, manganese, and lithium can be economically recovered from various lithium secondary battery-related wastes by the inventive method which comprises liquid-phase leaching a scrap powder containing Co, Ni, Mn, and Li, and purifying and solvent-extracting the resulting leaching solution to recover each of said Co, Ni, Mn, and Li, wherein the liquid-phase leaching is performed by a two-step counter-current leaching using an inorganic acid solution or a mixed solution of an inorganic acid and hydrogen peroxide.

Method for recovering valuable metals from lithium secondary battery wastes

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Seoul, South Korea

Bo Eun Kim