3 citations · 4 across the 8 of their papers we have counts for
9 papers
Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting
Juncheng Chen, Chao Xu, Yanjun Cao
Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) enables photorealistic novel-view s…
4D Neural Voxel Splatting: Dynamic Scene Rendering with Voxelized Guassian Splatting
Chun-Tin Wu, Jun-Cheng Chen
Although 3D Gaussian Splatting (3D-GS) achieves efficient rendering for novel view synthesis, extending it to dynamic scenes still results in substantial memory overhead from repli…
DRAG: Data Reconstruction Attack using Guided Diffusion
Wa-Kin Lei, Jun-Cheng Chen, Shang-Tse Chen
With the rise of large foundation models, split inference (SI) has emerged as a popular computational paradigm for deploying models across lightweight edge devices and cloud server…
FIPER: Factorized Features for Robust Image Super-Resolution and Compression
Yang-Che Sun, Cheng Yu Yeo, Ernie Chu +2
In this work, we propose using a unified representation, termed Factorized Features, for low-level vision tasks, where we test on Single Image Super-Resolution (SISR) and \textbf{I…
A Recipe for CAC: Mosaic-based Generalized Loss for Improved Class-Agnostic Counting
Tsung-Han Chou, Brian Wang, Wei-Chen Chiu +1
Class agnostic counting (CAC) is a vision task that can be used to count the total occurrence number of any given reference objects in the query image. The task is usually formulat…
Deep Complex U-Net with Conformer for Audio-Visual Speech Enhancement
Shafique Ahmed, Chia-Wei Chen, Wenze Ren +7
Recent studies have increasingly acknowledged the advantages of incorporating visual data into speech enhancement (SE) systems. In this paper, we introduce a novel audio-visual SE…
MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance
Ernie Chu, Tzuhsuan Huang, Shuo-Yen Lin +1
This study introduces an efficient and effective method, MeDM, that utilizes pre-trained image Diffusion Models for video-to-video translation with consistent temporal flow. The pr…
Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector
Shuo-Yen Lin, Ernie Chu, Che-Hsien Lin +2
Many physical adversarial patch generation methods are widely proposed to protect personal privacy from malicious monitoring using object detectors. However, they usually fail to g…
Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models
Ernie Chu, Shuo-Yen Lin, Jun-Cheng Chen
In this study, we present an efficient and effective approach for achieving temporally consistent synthetic-to-real video translation in videos of varying lengths. Our method lever…