11 citations · 26 across the 8 of their papers we have counts for
8 papers
Reciprocal Attention Mixing Transformer for Lightweight Image Restoration
Haram Choi, Cheolwoong Na, Jihyeon Oh +6
Although many recent works have made advancements in the image restoration (IR) field, they often suffer from an excessive number of parameters. Another issue is that most Transfor…
Randomized Adversarial Style Perturbations for Domain Generalization
Taehoon Kim, Bohyung Han
We propose a novel domain generalization technique, referred to as Randomized Adversarial Style Perturbation (RASP), which is motivated by the observation that the characteristics…
Cross-Class Feature Augmentation for Class Incremental Learning
Taehoon Kim, Jaeyoo Park, Bohyung Han
We propose a novel class incremental learning approach by incorporating a feature augmentation technique motivated by adversarial attacks. We employ a classifier learned in the pas…
Large-Scale Bidirectional Training for Zero-Shot Image Captioning
Taehoon Kim, Mark Marsden, Pyunghwan Ahn +4
When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To…
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
Jinsu Yoo, Taehoon Kim, Sihaeng Lee +3
Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods. However, these approaches suffer from essential short…
L-Verse: Bidirectional Generation Between Image and Text
Taehoon Kim, Gwangmo Song, Sihaeng Lee +6
Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially…
FrostNet: Towards Quantization-Aware Network Architecture Search
Taehoon Kim, YoungJoon Yoo, Jihoon Yang
INT8 quantization has become one of the standard techniques for deploying convolutional neural networks (CNNs) on edge devices to reduce the memory and computational resource usage…
Abstractive Text Classification Using Sequence-to-convolution Neural Networks
Taehoon Kim, Jihoon Yang
We propose a new deep neural network model and its training scheme for text classification. Our model Sequence-to-convolution Neural Networks(Seq2CNN) consists of two blocks: Seque…