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Pei-Chi Chen

dblp:24/7718 · DBLP profile ↗
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4ranked-venue papers
2as first author
2since 2021 · last 2025
0009-0005-4082-9301ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Vision and language · 77% Motion planning and robot control · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Vision and language › cross-modal alignment › visual-semantic alignment
vision-language representation alignment
0.812024
TrajPrompt: Aligning Color Trajectory with Vision-Language Representations · ECCV (41) 2024
Robotics › Motion planning and robot control
trajectory representation
0.212024
TrajPrompt: Aligning Color Trajectory with Vision-Language Representations · ECCV (41) 2024
YearPublicationVenuePosition
2025 Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
abstract
Camouflaged image generation is emerging as a solution to data scarcity in camouflaged vision perception, offering a cost-effective alternative to data collection and labeling. Recently, the state-of-the-art approach successfully generates camouflaged images using only foreground objects. However, it faces two critical weaknesses: 1) the background knowledge does not integrate effectively with foreground features, resulting in a lack of foreground-background coherence (e.g., color discrepancy); 2) the generation process does not prioritize the fidelity of foreground objects, which leads to distortion, particularly for small objects. To address these issues, we propose a Foreground-Aware Camouflaged Image Generation (FACIG) model. Specifically, we introduce a Foreground-Aware Feature Integration Module (FAFIM) to strengthen the integration between foreground features and background knowledge. In addition, a Foreground-Aware Denoising Loss is designed to enhance foreground reconstruction supervision. Experiments on various datasets show our method outperforms previous methods in overall camouflaged image quality and foreground fidelity.
Pei-Chi Chen, Chan-Feng Hsu, Hung-Jen Chen 0001, Hong-Han Shuai, Wen-Huang Cheng
ICME1
2024 TrajPrompt: Aligning Color Trajectory with Vision-Language Representations
Li-Wu Tsao, Hao-Tang Tsui, Yu-Rou Tuan, Pei-Chi Chen, Kuan-Lin Wang, Jhih-Ciang Wu, Hong-Han Shuai, Wen-Huang Cheng
ECCV (41)4
2012 Software Project Team Characteristics and Team Performance: Team Motivation as a Moderator
abstract
This study examines the relationship between software project team characteristics and team performance by focusing on two questions: whether or not different team characteristics in software project team affect project performance, and whether or not different types of team motivation lead to different level of influence to the relationship between team characteristics and performance? In this study, we argue that team motivation should be considered as a moderate between the relationship of team characteristics and performance. Different kind and level of team motivation can result in different project performance outcome. We examine the interaction effects between team characteristics and team motivation before their final impact to the performance. The results show that the relationship between project team characteristics and performance can be interfered with team motivation. Management should pay more attention to team motivation within project teams so that a better project performance could be achieved.
Pei-Chi Chen, Ching-Chin Chern, Chung Yang Chen
APSEC1
2009 A holistic approach to managing software change impact
Chung Yang Chen, Pei-Chi Chen
J. Syst. Softw.2