most citedFew-Shot Defect Image Generation via Defect-Aware Feature Manipulation

8 citations · 12 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2023

Painterly Image Harmonization via Adversarial Residual Learning

Xudong Wang, Li Niu, Junyan Cao +2

Image compositing plays a vital role in photo editing. After inserting a foreground object into another background image, the composite image may look unnatural and inharmonious. W…

cs.CV2023

DESOBAv2: Towards Large-scale Real-world Dataset for Shadow Generation

Qingyang Liu, Jianting Wang, Li Niu

Image composition refers to inserting a foreground object into a background image to obtain a composite image. In this work, we focus on generating plausible shadow for the inserte…

cs.CV2023

Foreground Object Search by Distilling Composite Image Feature

Bo Zhang, Jiacheng Sui, Li Niu

Foreground object search (FOS) aims to find compatible foreground objects for a given background image, producing realistic composite image. We observe that competitive retrieval p…

cs.CV20238 cited

Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation

Yuxuan Duan, Yan Hong, Li Niu +1

The performances of defect inspection have been severely hindered by insufficient defect images in industries, which can be alleviated by generating more samples as data augmentati…

cs.CV2022

Learning Object Placement via Dual-path Graph Completion

Siyuan Zhou, Liu Liu, Li Niu +1

Object placement aims to place a foreground object over a background image with a suitable location and size. In this work, we treat object placement as a graph completion problem…

cs.CV20221 cited

Few-shot Image Generation Using Discrete Content Representation

Yan Hong, Li Niu, Jianfu Zhang +1

Few-shot image generation and few-shot image translation are two related tasks, both of which aim to generate new images for an unseen category with only a few images. In this work…