Samsung Sds Co., Ltd.

Electronics and Telecommunications Research Institute

Xfc Inc.

Jong-Won Choi

Ji-Hoon Kim

Young-Joon Choi

Byoung-Jip Kim

Seong-Won Bak

Ju-Hyung Lee

Jin-Young Moon

Suk-Won Cha

Yong-Shin Hwang

Ki-Min Yun

Seok-Jun Choi

Joung-Su Youn

Woo-Seok Hyung

Disclosed herein are an object recognition apparatus and method based on environment matching. The object recognition apparatus includes memory for storing at least one program, and a processor for executing the program, wherein the program performs extracting at least one key frame from a video that is input in real time, determining a similarity between the key frame extracted from the input video and each of videos used as training data of prestored multiple recognition models, based on a pretrained similarity-matching network, and selecting a recognition model pretrained with a video having a maximal similarity to the key frame extracted from the input video, preprocessing the input video such that at least one of color and size of a video used as training data of an initial model is similar to that of the input video, and recognizing the preprocessed video based on the initial model.

Object recognition apparatus and method based on environment matching

A method for training a deep neural network according to an embodiment includes training a deep neural network model using a first data set including a plurality of labeled data and a second data set including a plurality of unlabeled data, assigning a ground-truth label value to some of the plurality of unlabeled data, updating the first data set and the second data set such that the data to which the ground-truth label value is assigned is included in the first data set, and further training the deep neural network model using the updated first data set and the updated second data set.

Apparatus and method for training deep neural network

A machine learning apparatus according to an embodiment includes a feature extractor configured to extract features from an object region of an image, a label processor configured to create sentence label embeddings from a sentence label corresponding to the object region, a first training data creator to extract first sub-features from a plurality of first sub-regions created by partitioning the object region, add the sentence label embeddings to the extracted first sub-features, and add the first sub-features added with the sentence label embeddings to the features of the object region, a second training data creator to extract a plurality of second sub-regions along a bounding surface of the object region, create an attention matrix from the second sub-regions, and create a training data by applying the attention matrix to the features of the object region, and a trainer to train an object detection model using the training data.

Machine learning apparatus and method for object detection

Provided are an optimization system and method of a deep learning model. According to example embodiments, by optimizing the structure of the deep learning model appropriately for a target dataset without fixing the structure of the deep learning model, it is possible to generate a model structure capable of having high performance on the target dataset and also saving resources.

System and method for optimization of deep learning model

An image processing apparatus includes a superpixel extractor configured to extract a plurality of superpixels from an input original image, a backbone network including N feature extracting layers (here, N is a natural number of two or more) which divide the input original image into grids including a plurality of regions and generate an output value including a feature value for each of the divided regions, and a superpixel pooling layer configured to generate a superpixel feature value corresponding to each of the plurality of superpixels using a first output value to an Noutput value output from each of the N feature extracting layers.

Apparatus and method for image processing for machine learning

A method of training a model for object classification and detection includes training a first classification model including a shared feature extractor shared by classification models and a first classifier for outputting a result of an object in a first input image based on feature values of the first input image, training a second classification model including the shared feature extractor and a second classifier for outputting a result about authenticity of a second input image based on feature values of the second input image, and training a third classification model including the shared feature extractor and a third classifier for outputting a classification result about a rotation angle of a third input image on the basis of feature values of the third input image extracted by the shared feature extractor, using a third training image set including images rotated at one or more angles.

Method and apparatus for training model for object classification and detection

A separator for a fuel cell is provided. The separator comprises a flow path member and a base separator. The flow path member is made of a graphite foil and has a flow path through which fluids pass. The base separator has a seating recess formed on the surface thereof. The flow path member is mounted in the seating recess. The flow path is formed by pressing the graphite foil. The separator can be produced at reduced processing cost in a short processing time.

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Seoul, South Korea

Jong-Won Choi