Search Results for author: Minchul Shin

Found 13 papers, 3 papers with code

Multi-Modal Mixup for Robust Fine-tuning

no code implementations8 Mar 2022 Junhyuk So, Changdae Oh, Minchul Shin, Kyungwoo Song

Less robust embedding might restrict the transferability of the representation for the downstream task.

Contrastive Learning

Boundary-aware Self-supervised Learning for Video Scene Segmentation

1 code implementation14 Jan 2022 Jonghwan Mun, Minchul Shin, Gunsoo Han, Sangho Lee, Seongsu Ha, Joonseok Lee, Eun-Sol Kim

Inspired from this, we tackle video scene segmentation, which is a task of temporally localizing scene boundaries in a video, with a self-supervised learning framework where we mainly focus on designing effective pretext tasks.

Scene Segmentation Self-Supervised Learning

Winning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts

no code implementations13 Oct 2021 Minchul Shin, Jonghwan Mun, Kyoung-Woon On, Woo-Young Kang, Gunsoo Han, Eun-Sol Kim

The VALUE (Video-And-Language Understanding Evaluation) benchmark is newly introduced to evaluate and analyze multi-modal representation learning algorithms on three video-and-language tasks: Retrieval, QA, and Captioning.

Representation Learning Transfer Learning

Boundary-aware Pre-training for Video Scene Segmentation

no code implementations29 Sep 2021 Jonghwan Mun, Minchul Shin, Gunsoo Han, Sangho Lee, Seongsu Ha, Joonseok Lee, Eun-Sol Kim

Inspired from this, we tackle video scene segmentation, which is a task of temporally localizing scene boundaries in a video, with a self-supervised learning framework where we mainly focus on designing effective pretext tasks.

Scene Segmentation Self-Supervised Learning

Towards Light-weight and Real-time Line Segment Detection

2 code implementations1 Jun 2021 Geonmo Gu, Byungsoo Ko, SeoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin

In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD).

Line Segment Detection

Fashion-IQ 2020 Challenge 2nd Place Team's Solution

no code implementations13 Jul 2020 Minchul Shin, Yoonjae Cho, Seongwuk Hong

This paper is dedicated to team VAA's approach submitted to the Fashion-IQ challenge in CVPR 2020.

A Benchmark on Tricks for Large-scale Image Retrieval

no code implementations27 Jul 2019 Byungsoo Ko, Minchul Shin, Geonmo Gu, HeeJae Jun, Tae Kwan Lee, Youngjoon Kim

Many studies have been performed on metric learning, which has become a key ingredient in top-performing methods of instance-level image retrieval.

Image Retrieval Metric Learning

Semi-supervised Feature-Level Attribute Manipulation for Fashion Image Retrieval

no code implementations11 Jul 2019 Minchul Shin, Sanghyuk Park, Taeksoo Kim

FAM is a challenging task in that the attributes are hard to define, and the unique characteristics of a query are hard to be preserved.

Image Retrieval

Baseline CNN structure analysis for facial expression recognition

no code implementations14 Nov 2016 Minchul Shin, Munsang Kim, Dong-Soo Kwon

The experiment result showed that a three-layer structure consisting of a simple convolutional and a max pooling layer with histogram equalization image input was the most efficient.

Facial Expression Recognition

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