Search Results for author: Chaerin Kong

Found 9 papers, 2 papers with code

ConcatPlexer: Additional Dim1 Batching for Faster ViTs

no code implementations22 Aug 2023 Donghoon Han, Seunghyeon Seo, Donghyeon Jeon, Jiho Jang, Chaerin Kong, Nojun Kwak

Transformers have demonstrated tremendous success not only in the natural language processing (NLP) domain but also the field of computer vision, igniting various creative approaches and applications.

AADiff: Audio-Aligned Video Synthesis with Text-to-Image Diffusion

no code implementations6 May 2023 Seungwoo Lee, Chaerin Kong, Donghyeon Jeon, Nojun Kwak

Recent advances in diffusion models have showcased promising results in the text-to-video (T2V) synthesis task.

Analyzing Multimodal Objectives Through the Lens of Generative Diffusion Guidance

no code implementations10 Feb 2023 Chaerin Kong, Nojun Kwak

Recent years have witnessed astonishing advances in the field of multimodal representation learning, with contrastive learning being the cornerstone for major breakthroughs.

Contrastive Learning Representation Learning

Unifying Vision-Language Representation Space with Single-tower Transformer

no code implementations21 Nov 2022 Jiho Jang, Chaerin Kong, Donghyeon Jeon, Seonhoon Kim, Nojun Kwak

Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations.

Contrastive Learning Object Localization +3

Leveraging Off-the-shelf Diffusion Model for Multi-attribute Fashion Image Manipulation

no code implementations12 Oct 2022 Chaerin Kong, Donghyeon Jeon, Ohjoon Kwon, Nojun Kwak

Fashion attribute editing is a task that aims to convert the semantic attributes of a given fashion image while preserving the irrelevant regions.

Attribute Image Manipulation

Towards Efficient Neural Scene Graphs by Learning Consistency Fields

no code implementations9 Oct 2022 Yeji Song, Chaerin Kong, Seoyoung Lee, Nojun Kwak, Joonseok Lee

Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it to dynamic scenes (video) with multiple objects.

Conservative Generator, Progressive Discriminator: Coordination of Adversaries in Few-shot Incremental Image Synthesis

no code implementations29 Jul 2022 Chaerin Kong, Nojun Kwak

The capacity to learn incrementally from an online stream of data is an envied trait of human learners, as deep neural networks typically suffer from catastrophic forgetting and stability-plasticity dilemma.

Few-Shot Learning Image Generation +1

Self-Distilled Self-Supervised Representation Learning

1 code implementation25 Nov 2021 Jiho Jang, Seonhoon Kim, KiYoon Yoo, Chaerin Kong, Jangho Kim, Nojun Kwak

Through self-distillation, the intermediate layers are better suited for instance discrimination, making the performance of an early-exited sub-network not much degraded from that of the full network.

Representation Learning Self-Supervised Learning

Few-shot Image Generation with Mixup-based Distance Learning

1 code implementation23 Nov 2021 Chaerin Kong, Jeesoo Kim, Donghoon Han, Nojun Kwak

Producing diverse and realistic images with generative models such as GANs typically requires large scale training with vast amount of images.

Image Generation

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