7 papers · 1 filter
StAR: Segment Anything Reasoner
Seokju Yun, Dongheon Lee, Noori Bae +3
As AI systems are being integrated more rapidly into diverse and complex real-world environments, the ability to perform holistic reasoning over an implicit query and an image to l…
Emulating Self-attention with Convolution for Efficient Image Super-Resolution
Dongheon Lee, Seokju Yun, Youngmin Ro
In this paper, we tackle the high computational overhead of Transformers for efficient image super-resolution~(SR). Motivated by the observations of self-attention's inter-layer re…
SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning
Seokju Yun, Seunghye Chae, Dongheon Lee +1
Domain generalization (DG) aims to adapt a model using one or multiple source domains to ensure robust performance in unseen target domains. Recently, Parameter-Efficient Fine-Tuni…
Implicit Grid Convolution for Multi-Scale Image Super-Resolution
Dongheon Lee, Seokju Yun, Youngmin Ro
For Image Super-Resolution (SR), it is common to train and evaluate scale-specific models composed of an encoder and upsampler for each targeted scale. Consequently, many SR studie…
FFNet: MetaMixer-based Efficient Convolutional Mixer Design
Seokju Yun, Dongheon Lee, Youngmin Ro
Transformer, composed of self-attention and Feed-Forward Network, has revolutionized the landscape of network design across various vision tasks. While self-attention is extensivel…
Partial Large Kernel CNNs for Efficient Super-Resolution
Dongheon Lee, Seokju Yun, Youngmin Ro
Recently, in the super-resolution (SR) domain, transformers have outperformed CNNs with fewer FLOPs and fewer parameters since they can deal with long-range dependency and adaptive…