6 citations · 11 across the 9 of their papers we have counts for
10 papers
RecycleLoRA: Rank-Revealing QR-Based Dual-LoRA Subspace Adaptation for Domain Generalized Semantic Segmentation
Chanseul Cho, Seokju Yun, Jeaseong Jeon +2
Domain Generalized Semantic Segmentation (DGSS) aims to maintain robust performance across unseen target domains. Vision Foundation Models (VFMs) offer rich multi-domain knowledge…
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…
Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention
Dongheon Lee, Seokju Yun, Jaegyun Im +1
Recent Super-Resolution~(SR) methods mainly adopt Transformers for their strong long-range modeling capability and exceptional representational capacity. However, most SR Transform…
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…
SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design
Seokju Yun, Youngmin Ro
Recently, efficient Vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally, they use 4x4 patch embeddings and a 4-stage s…
Dynamic Mobile-Former: Strengthening Dynamic Convolution with Attention and Residual Connection in Kernel Space
Seokju Yun, Youngmin Ro
We introduce Dynamic Mobile-Former(DMF), maximizes the capabilities of dynamic convolution by harmonizing it with efficient operators.Our Dynamic MobileFormer effectively utilizes…