6 citations · 7 across the 10 of their papers we have counts for
11 papers
OV-Stitcher: A Global Context-Aware Framework for Training-Free Open-Vocabulary Semantic Segmentation
Seungjae Moon, Seunghyun Oh, Youngmin Ro
Training-free open-vocabulary semantic segmentation(TF-OVSS) has recently attracted attention for its ability to perform dense prediction by leveraging the pretrained knowledge of…
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…
Arbitrary-Scale Downscaling of Tidal Current Data Using Implicit Continuous Representation
Dongheon Lee, Seungmyong Jeong, Youngmin Ro
Numerical models have long been used to understand geoscientific phenomena, including tidal currents, crucial for renewable energy production and coastal engineering. However, thei…
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…