Search Results for author: Minho Shim

Found 6 papers, 1 papers with code

Masked Autoencoder for Unsupervised Video Summarization

no code implementations2 Jun 2023 Minho Shim, Taeoh Kim, Jinhyung Kim, Dongyoon Wee

Summarizing a video requires a diverse understanding of the video, ranging from recognizing scenes to evaluating how much each frame is essential enough to be selected as a summary.

Self-Supervised Learning Unsupervised Video Summarization

Exploring Temporally Dynamic Data Augmentation for Video Recognition

no code implementations30 Jun 2022 Taeoh Kim, Jinhyung Kim, Minho Shim, Sangdoo Yun, Myunggu Kang, Dongyoon Wee, Sangyoun Lee

The magnitude of augmentation operations on each frame is changed by an effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and realistic temporal variations.

Action Segmentation Image Augmentation +3

Frequency Selective Augmentation for Video Representation Learning

no code implementations8 Apr 2022 Jinhyung Kim, Taeoh Kim, Minho Shim, Dongyoon Han, Dongyoon Wee, Junmo Kim

FreqAug stochastically removes specific frequency components from the video so that learned representation captures essential features more from the remaining information for various downstream tasks.

Action Recognition Data Augmentation +3

Teaching Machines to Understand Baseball Games: Large-Scale Baseball Video Database for Multiple Video Understanding Tasks

no code implementations ECCV 2018 Minho Shim, Young Hwi Kim, Kyung-Min Kim, Seon Joo Kim

A major obstacle in teaching machines to understand videos is the lack of training data, as creating temporal annotations for long videos requires a huge amount of human effort.

Video Alignment Video Recognition

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