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Haoda Huang

dblp:00/2240 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 3Artificial intelligence and machine learning · 1

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
Learning paradigms · 77% Generative modeling · 23%

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

TopicWeightPapersLastEvidence papers
Machine learning › Learning paradigms › unsupervised learning
unsupervised structure learning
0.112008
Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion · ECCV (2) 2008
Machine learning › Generative modeling › variational autoencoder › hierarchical latent variable model
hierarchical compositional model
0.012008
Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion · ECCV (2) 2008

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

suspicious coincidence · 0.1competitive exclusion · 0.1
YearPublicationVenuePosition
2013 Intent-aware image cloning
Xiaohui Bie, Wencheng Wang 0001, Hanqiu Sun, Haoda Huang, Minying Zhang
Vis. Comput.4
2011 Real Time Edit Propagation by Efficient Sampling
abstract
Abstract It is popular to edit the appearance of images using strokes, owing to their ease of use and convenience of conveying the user's intention. However, propagating the user inputs to the rest of the images requires solving an enormous optimization problem, which is very time consuming, thus preventing its practical use. In this paper, a two‐step edit propagation scheme is proposed, first to solve edits on clusters of similar pixels and then to interpolate individual pixel edits from cluster edits. The key in our scheme is that we use efficient stroke sampling to compute the affinity between image pixels and strokes. Based on this, our clustering does not need to be stroke‐adaptive and thus the number of clusters is greatly reduced, resulting in a significant speedup. The proposed method has been tested on various images, and the results show that it is more than one order of magnitude faster than existing methods, while still achieving precise results compared with the ground truth. Moreover, its efficiency is not sensitive to the number of strokes, making it suitable for performing dense edits in practice.
Xiaohui Bie, Haoda Huang, Wencheng Wang 0001
Comput. Graph. Forum2
2008 Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion
Long Zhu, Haoda Huang, Yuanhao Chen, Alan L. Yuille
ECCV (2)3