277 citations · 1.6k across the 56 of their papers we have counts for
13 papers · 1 filter
Generator Knows What Discriminator Should Learn in Unconditional GANs
Gayoung Lee, Hyunsu Kim, Junho Kim +3
Recent methods for conditional image generation benefit from dense supervision such as segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ de…
Time Is MattEr: Temporal Self-supervision for Video Transformers
Sukmin Yun, Jaehyung Kim, Dongyoon Han +3
Understanding temporal dynamics of video is an essential aspect of learning better video representations. Recently, transformer-based architectural designs have been extensively ex…
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
Jiyeon Han, Hwanil Choi, Yunjey Choi +3
Evaluation metrics in image synthesis play a key role to measure performances of generative models. However, most metrics mainly focus on image fidelity. Existing diversity metrics…
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
Jihwan Bang, Hyunseo Koh, Seulki Park +3
Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. A large body of continual learning (CL) methods, h…
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
Sihyun Yu, Jihoon Tack, Sangwoo Mo +4
In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Existing prior works have…
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks
Yunji Kim, Jung-Woo Ha
Unsupervised fine-grained class clustering is a practical yet challenging task due to the difficulty of feature representations learning of subtle object details. We introduce C3-G…
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo +2
Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realisti…
M2FN: Multi-step Modality Fusion for Advertisement Image Assessment
Kyung-Wha Park, Jung-Woo Ha, JungHoon Lee +3
Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neu…
StarGAN v2: Diverse Image Synthesis for Multiple Domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo +1
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) s…
Photorealistic Style Transfer via Wavelet Transforms
Jaejun Yoo, Youngjung Uh, Sanghyuk Chun +2
Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unre…
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Yunjey Choi, Minje Choi, Munyoung Kim +3
Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more th…
Dual Attention Networks for Multimodal Reasoning and Matching
Hyeonseob Nam, Jung-Woo Ha, Jeonghee Kim
We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language. DANs attend…
Multimodal Residual Learning for Visual QA
Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak +4
Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limite…