Samsung Electronics Co., Ltd.

Korea Advanced Institute of Science and Technology

Snu Rdb Foundation

Chinese Academy of Sciences

University of Illinois

Northeastern University

University of Toronto

Jaejoon Han

Seungju Han

Chang Kyu Choi

Byungin Yoo

Changyong Son

Minsu Ko

Jinwoo Son

Jungbae Kim

Seon Min Rhee

Jihye Kim

Sungjoo Suh

Youngjun Kwak

Sungun Park

Wonjun Hwang

Deoksang Kim

Byung In Yoo

Wonsuk Chang

Kyuhong Kim

Wonjun Kim

Changkyo Lee

Insoo Kim

Jingtao Xu

Dae-kyu Shin

Heewon Kim

Jiwon Baek

Youngsung Kim

Sangil Jung

Jiaqian Yu

Seong-Jin Park

Jaehyoung Yoo

Qiang Wang

Hui Zhang

Changkyu Choi

Hao Feng

Hana Lee

Hyunjeong Lee

Sung-Jae Cho

Youngkyoo Hwang

Ju Hwan Song

Yiwei Chen

Chao Zhang

Dongwook Lee

Jiwhan Kim

Namjoon Kim

Changbeom Park

Seohyung Lee

HyunJeong Lee

Tianchu Guo

Xiabing Liu

Changin Choi

Yongchao Liu

Jongseok Kim

Jiho Choi

Solae Lee

Yong-Il Lee

Biao Wang

Bohyung Han

Hee Chul Jung

Jun Mo Kim

Sungheon Park

Yanhu Shan

Jaeyun Lee

Jun Ho Yim

Bing Yu

Liang Wang

Joonah Park

Meng Ye

Fei Duan

Yun Fu

Kiho Cho

Sung Kwang Cho

Xuetao Feng

Yongjie Zhang

Hongwei Zhang

Lujin Gong

Feng Qiu

Hui Li

Yongzhen Huang

Haoxing Wang

Juhwan Song

Hangkai Tan

Sung Ju Hwang

Seonmin Rhee

Dohwan Lee

Raymond Alexander Yeh

Alexander Gerhard Schwing

Zhengming Ding

Geuntae Bae

Jiguang Xue

Byungln Yoo

Ming Shao

Ruslan Salakhutdinov

Colin Graber

Kikyung Kim

Sung-jae Cho

Chyangbeom Park

Seoh Yung Lee

A method with face recognition includes: determining a first global feature of a first face image and a first global feature of a second face image based on a local feature of the first face image and a local feature of the second face image, respectively; determining a final global feature of the first face image based on the first global feature of the first face image and a second global feature of the first face image; determining a final global feature of the second face image based on the first global feature and a second global feature of the second face image; and recognizing the first face image and the second face image based on the final global feature of the first face image and the final global feature of the second face image, wherein the second global feature of the first face image is determined based on the local feature of the first face image, and the second global feature of the second face image is determined based on the local feature of the second face image.

Method and apparatus with face recognition

A method with object tracking includes: determining a first target tracking state by tracking a target from a first image frame with a first field of view (FoV); determining a second FoV based on the first FoV and the first target tracking state; and generating a second target tracking result by tracking a target from a second image frame with the second FoV.

Method and apparatus with object tracking using dynamic field of view

A neural network processing method and apparatus based on nested bit representation is provided. The processing method includes obtaining first weights for a first layer of a source model of a first layer of a neural network, determining a bit-width for the first layer of the neural network, obtaining second weights for the first layer of the neural network by extracting at least one bit corresponding to the determined bit-width from each of the first weights for the first layer of a source model corresponding to the first layer of the neural network, and processing input data of the first layer of the neural network by executing the first layer of the neural network based on the obtained second weights.

Neural network processing method and apparatus based on nested bit representation

Provided is a method and apparatus with neural network operation. The method includes generating a first intermediate operation result by performing a first-order partial operation of a neural network layer on a first input line of a first area of a frame, generating a second intermediate operation result by performing another first-order partial operation of the neural network layer on a second input line of the first area, and generating an objective operation result of the neural network layer with respect to the first area based on a second-order partial operation performed on the first intermediate operation result and the second intermediate operation result.

Method and apparatus with neural network operation

A method with biometric information spoof detection includes extracting an embedding vector from an intermediate layer of a neural network configured to detect whether biometric information of a user is spoofed from an image including the biometric information; detecting first information regarding whether the biometric information is spoofed, based on the embedding vector; and detecting second information regarding whether the biometric information is spoofed based on whether the first information is detected, using an output vector output from an output layer of the neural network.

Method and apparatus with biometric information spoof detection

Disclosed is an image fusion method and apparatus. The fusion method includes detecting first feature points of an object in a first image frame from the first image frame; transforming the first image frame based on the detected first feature points and predefined reference points to generate a transformed first image frame; detecting second feature points of the object in a second image frame from the second image frame; transforming the second image frame based on the detected second feature points and the predefined reference points to generate a transformed second image frame; and generating a combined image by combining the transformed first image frame and the transformed second image frame.

Method and apparatus with image fusion

A processor-implemented method with image preprocessing includes: transforming a fingerprint image into a frequency domain; suppressing a low-frequency region corresponding to a first noise component of the fingerprint image in the frequency domain; and restoring the frequency domain, in which the low-frequency region is suppressed, as an image.

Method and apparatus with image preprocessing

A processor-implemented method with liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

Method and apparatus with liveness detection

An authentication method and apparatus using a transformation model are disclosed. The authentication method includes generating, at a first apparatus, a first enrolled feature based on a first feature extractor, obtaining a second enrolled feature to which the first enrolled feature is transformed, determining an input feature by extracting a feature from input data with a second feature extractor different from the first feature extractor, and performing an authentication based on the second enrolled feature and the input feature.

Authentication method and apparatus with transformation model

A target object detection method and apparatus are provided. The target object detection method and apparatus are applicable to fields such as artificial intelligence, object tracking, object detection, and image processing. An object is detected from a frame image of a video including a plurality of frame images based on a target template set including one or more target templates.

Video object detection and tracking method and apparatus

An image recognition method includes: receiving an input image of a first quality; extracting an input feature of a second quality of the input image from the input image by inputting the input image to an encoding model in an image recognizing model; and generating a recognition result for the input image based on the input feature.

Method and apparatus with image recognition

A processor-implemented method with input data classification includes: extracting an input embedding vector including a feature of biometric information of a user from input data including the biometric information; determining an adaptive embedding vector adaptive to the input embedding vector, based on a combination of a plurality of enrollment embedding vectors that are based on enrollment data; and classifying the input data based on a similarity between the input embedding vector and the adaptive embedding vector.

Method and apparatus with input data classification

An image processing method includes receiving an input image and a guide image corresponding to the input image, extracting informative features from the input image and the guide image to enhance the input image, selectively obtaining a first feature for the input image from among the informative features, and processing the input image based on the first feature.

Image processing method and image processing apparatus

A processor-implemented radar data recognition method including generating a plurality of pieces of input data, with respectively different dimension configurations, based on radar data of an object; and outputting a recognition result of the object based on the generated plurality of pieces of input data using a recognition model.

Method and apparatus with radar data recognition

A method and apparatus for generating a depth image are provided. The apparatus receives an input image, extracts a feature corresponding to the input image, generates features for each depth resolution by decoding the feature using decoders corresponding to different depth resolutions, estimates probability distributions for each depth resolution by progressively refining the features for each depth resolution, and generates a target depth image corresponding to the input image based on a final estimated probability distribution from among the probability distributions for each depth resolution.

Method and apparatus for generating depth image

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Seoul, South Korea

Jaejoon Han