Q
Qin Huang
Researcher at University of Southern California
Publications - 26
Citations - 813
Qin Huang is an academic researcher from University of Southern California. The author has contributed to research in topics: Segmentation & Object (computer science). The author has an hindex of 12, co-authored 26 publications receiving 576 citations. Previous affiliations of Qin Huang include Facebook.
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Book ChapterDOI
Contextual-Based Image Inpainting: Infer, Match, and Translate
TL;DR: This work proposes a learning-based approach to generate visually coherent completion given a high-resolution image with missing components and shows that it generates results of better visual quality than previous state-of-the-art methods.
Proceedings ArticleDOI
Instance Embedding Transfer to Unsupervised Video Object Segmentation
TL;DR: In this paper, an instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object in consecutive video frames, which allows to link objects together over time.
Posted Content
Contextual-based Image Inpainting: Infer, Match, and Translate
TL;DR: In this article, a learning-based approach is proposed to generate visually coherent completion given a high-resolution image with missing components, which divides the task into inference and translation as two separate steps and models each step with a deep neural network.
Proceedings Article
SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting
TL;DR: This paper proposes to introduce the semantic segmentation information, which disentangles the inter-class difference and intra-class variation for image inpainting, which leads to much clearer recovered boundary between semantically different regions and better texture within semantically consistent segments.
Posted Content
SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting
TL;DR: Zhang et al. as discussed by the authors propose to use semantic segmentation information to disentangle the inter-class difference and intra-class variation for image inpainting, which leads to much clearer recovered boundary between semantically different regions and better texture within semantically consistent segments.