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Kaile Zhang

dblp:199/7653 · DBLP profile ↗
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4ranked-venue papers
1as first author
3since 2021 · last 2025
—ORCID · conflict

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

Artificial intelligence and machine learning · 4 · 1 first-author · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 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
Generative modeling · 100%
Computer graphics and multimedia
1 paper
Visual content generation and editing · 77% Image and video processing · 23%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
diffusion model
0.912025
Training-Free Geometric Image Editing on Diffusion Models · ICCV 2025
Machine learning › Generative modeling › diffusion model › image editing
training-free image editing
0.912025
Training-Free Geometric Image Editing on Diffusion Models · ICCV 2025
Visual content generation and editing
image editing
0.912025
Training-Free Geometric Image Editing on Diffusion Models · ICCV 2025
Image and video processing › image restoration
image inpainting
0.312025
Training-Free Geometric Image Editing on Diffusion Models · ICCV 2025

Methods — techniques the papers use, named apart from their topics

training-free editing · 1.7diffusion model · 1.7
YearPublicationVenuePosition
2025 Training-Free Geometric Image Editing on Diffusion Models
abstract
We tackle the task of geometric image editing, where an object within an image is repositioned, reoriented, or reshaped while preserving overall scene coherence. Previous diffusion-based editing methods often attempt to handle all relevant subtasks in a single step, proving difficult when transformations become large or structurally complex. We address this by proposing a decoupled pipeline that separates object transformation, source region inpainting, and target region refinement. Both inpainting and refinement are implemented using a training-free diffusion approach, FreeFine. In experiments on our new GeoBench benchmark, which contains both 2D and 3D editing scenarios, FreeFine outperforms state-of-the-art alternatives in image fidelity, and edit precision, especially under demanding transformations. Code and benchmark are available at: https://github.com/CIawevy/FreeFine
Hanshen Zhu, Zhen Zhu 0006, Kaile Zhang, Yiming Gong, Xiang Bai
ICCV3
2024 Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstruction
Pengjie Wang 0005, Kaile Zhang, Xinyu Wang 0010, Shengwei Han, Yongge Liu, Xiang Bai
ICDAR (1)2
2024 Semantics Outperforms Prosody in Emotional Speech Processing: Evidence from a Complex Stroop Experiment
Kaile Zhang, Gang Peng 0001
PACLIC2
2017 The Relationship Between the Perception and Production of Non-Native Tones
abstract
2017-2018 > Academic research: refereed > Refereed conference paper
Kaile Zhang, Gang Peng 0001
INTERSPEECH1