3 citations · 8 across the 13 of their papers we have counts for
18 papers
Hierarchical and Query-Aware Visual Token Pruning for High-Resolution LVLMs
Luther Wei, Ling Zou, Kang-Yang Huang +2
High-resolution Large Vision-Language Models (LVLMs) preserve critical, fine-grained visual details by encoding a global thumbnail alongside multiple local crops. However, the resu…
ECOKV: Geometry-Aware KV Cache Eviction via Complementary Diversity Metrics
Chin Ting Hsu, Yu-Syuan Xu, Ling Zou +2
Although multimodal Large Language Models (MLLMs) excel in diverse tasks, their scalability remains limited by the memory and computational overhead of KV cache storage. Recent KV…
Fidelity- and Perception-Aware Local Implicit Attention for Arbitrary-Scale Image Super-Resolution
Yu-Syuan Xu, Hao-Lun Sun, Hao-Wei Chen +2
Arbitrary-scale image super-resolution (ASISR) aims to reconstruct high-resolution images from low-resolution inputs over a continuous range of upscaling factors. While traditional…
The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
Jiatong Li, Zheng Chen, Kai Liu +91
This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge…
Bridging Restoration and Generation in One-step Diffusion for Real-World Image Super-Resolution
Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu +3
Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohibitive, driving efforts to distil…
Learning Ordinal Degradation Representations with Textual Priors for Diffusion-Based Blind Image Super-Resolution
Yi-Cheng Liao, Shyang-En Weng, Yu-Syuan Xu +3
Blind image super-resolution (Blind SR) has achieved remarkable perceptual quality via generative priors. However, lacking clear degradation representations such as varying severit…
Two Heads Better than One: Dual Degradation Representation for Blind Super-Resolution
Hsuan Yuan, Shao-Yu Weng, I-Hsuan Lo +5
Previous methods have demonstrated remarkable performance in single image super-resolution (SISR) tasks with known and fixed degradation (e.g., bicubic downsampling). However, when…
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Yu-Cheng Lin, Yu-Syuan Xu, Hao-Wei Chen +2
Image restoration is a key task in low-level computer vision that aims to reconstruct high-quality images from degraded inputs. The emergence of Vision Mamba, which draws inspirati…
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…
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…
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning
Andrey Ignatov, Anastasia Sycheva, Radu Timofte +8
While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very…
PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks
Andrey Ignatov, Grigory Malivenko, Radu Timofte +8
The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile…
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
Andrey Ignatov, Radu Timofte, Cheng-Ming Chiang +50
Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While num…
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Cheng-Ming Chiang, Hsien-Kai Kuo +38
As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual…
Network Space Search for Pareto-Efficient Spaces
Min-Fong Hong, Hao-Yun Chen, Min-Hung Chen +5
Network spaces have been known as a critical factor in both handcrafted network designs or defining search spaces for Neural Architecture Search (NAS). However, an effective space…
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
Cheng-Ming Chiang, Yu Tseng, Yu-Syuan Xu +13
Recently, image enhancement and restoration have become important applications on mobile devices, such as super-resolution and image deblurring. However, most state-of-the-art netw…
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations
Yu-Syuan Xu, Shou-Yao Roy Tseng, Yu Tseng +2
Deep Convolutional Neural Networks (CNNs) have achieved remarkable results on Single Image Super-Resolution (SISR). Despite considering only a single degradation, recent studies al…
Dynamic Video Segmentation Network
Yu-Syuan Xu, Tsu-Jui Fu, Hsuan-Kung Yang +1
In this paper, we present a detailed design of dynamic video segmentation network (DVSNet) for fast and efficient semantic video segmentation. DVSNet consists of two convolutional…