most citedCascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

2 citations · 2 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV2024

HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior

Li-Yuan Tsao, Hao-Wei Chen, Hao-Wei Chung +4

Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noi…

cs.CV2024

AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters

Hao-Wei Chen, Yu-Syuan Xu, Kelvin C. K. Chan +3

Existing image restoration approaches typically employ extensive networks specifically trained for designated degradations. Despite being effective, such methods inevitably entail…

cs.RO20231 cited

Learning to Terminate in Object Navigation

Yuhang Song, Anh Nguyen, Chun-Yi Lee

This paper tackles the critical challenge of object navigation in autonomous navigation systems, particularly focusing on the problem of target approach and episode termination in…

cs.CV202320 cited

MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

Yuki Kondo, Norimichi Ukita, Takayuki Yamaguchi +19

Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a ch…

cs.MA2023

A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

Wei-Fang Sun, Cheng-Kuang Lee, Simon See +1

In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously chan…

cs.CV20232 cited

Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong +3

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT…