162 citations · 596 across the 62 of their papers we have counts for
12 papers · 1 filter
Disposable Transfer Learning for Selective Source Task Unlearning
Seunghee Koh, Hyounguk Shon, Janghyeon Lee +2
Transfer learning is widely used for training deep neural networks (DNN) for building a powerful representation. Even after the pre-trained model is adapted for the target task, th…
Generative Approach for Probabilistic Human Mesh Recovery using Diffusion Models
Hanbyel Cho, Junmo Kim
This work focuses on the problem of reconstructing a 3D human body mesh from a given 2D image. Despite the inherent ambiguity of the task of human mesh recovery, most existing work…
Implicit 3D Human Mesh Recovery using Consistency with Pose and Shape from Unseen-view
Hanbyel Cho, Yooshin Cho, Jaesung Ahn +1
From an image of a person, we can easily infer the natural 3D pose and shape of the person even if ambiguity exists. This is because we have a mental model that allows us to imagin…
Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery
Hyungmin Kim, Sungho Suh, Daehwan Kim +3
Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learni…
The Effects of Mixed Sample Data Augmentation are Class Dependent
Haeil Lee, Hansang Lee, Junmo Kim
Mixed Sample Data Augmentation (MSDA) techniques, such as Mixup, CutMix, and PuzzleMix, have been widely acknowledged for enhancing performance in a variety of tasks. A previous st…
Deep Cross-Modal Steganography Using Neural Representations
Gyojin Han, Dong-Jae Lee, Jiwan Hur +2
Steganography is the process of embedding secret data into another message or data, in such a way that it is not easily noticeable. With the advancement of deep learning, Deep Neur…