6 papers · 1 filter
MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation
Saad Wazir, Patrick Dominique Vibild, Dinh Phu Tran +2
Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level predictions under challenging…
FreqOrtho-SR: Frequency-Guided Orthogonal Expert Learning for Real-World Image Super-Resolution
Minh Son Hoang, Dinh Phu Tran, Quyen Nguyen Duc +2
Diffusion prior-based methods have shown impressive results in real-world image super-resolution (ISR), yet two key challenges persist: balancing pixel-level fidelity with semantic…
TB-AVA: Text as a Semantic Bridge for Audio-Visual Parameter Efficient Finetuning
Seongah Kim, Dinh Phu Tran, Hyeontaek Hwang +3
Audio-visual understanding requires effective alignment between heterogeneous modalities, yet cross-modal correspondence remains challenging when temporally aligned audio and visua…
SAT: Selective Aggregation Transformer for Image Super-Resolution
Dinh Phu Tran, Thao Do, Saad Wazir +3
Transformer-based approaches have revolutionized image super-resolution by modeling long-range dependencies. However, the quadratic computational complexity of vanilla self-attenti…
VSRM: A Robust Mamba-Based Framework for Video Super-Resolution
Dinh Phu Tran, Dao Duy Hung, Daeyoung Kim
Video super-resolution remains a major challenge in low-level vision tasks. To date, CNN- and Transformer-based methods have delivered impressive results. However, CNNs are limited…
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution
Dinh Phu Tran, Dao Duy Hung, Daeyoung Kim
Recently, window-based attention methods have shown great potential for computer vision tasks, particularly in Single Image Super-Resolution (SISR). However, it may fall short in c…