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- Nanjing UniversityCN20 papers
- University of Science and Technology of ChinaCN20 papers
- Joint Institute for Nuclear ResearchRU19 papers
- Shandong UniversityCN19 papers
- Indiana University BloomingtonUS17 papers
- Budker Institute of Nuclear PhysicsRU16 papers
- Johannes Gutenberg University MainzDE16 papers
- Doğuş UniversityTR15 papers
- Ankara UniversityTR14 papers
- Tsinghua UniversityCN14 papers
- University of Illinois Urbana-ChampaignUS13 papers
- Brookhaven National LaboratoryUS12 papers
18 papers · 2 filters
Matching-CNN Meets KNN: Quasi-Parametric Human Parsing
Si Liu, Xiaodan Liang, Luoqi Liu +6
Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e…
Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model
Xiaodan Liang, Qingxing Cao, Rui Huang +1
The aim of this study is to provide an automatic computational framework to assist clinicians in diagnosing Focal Liver Lesions (FLLs) in Contrast-Enhancement Ultrasound (CEUS). We…
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars
Xionghao Liu, Wei Yang, Liang Lin +3
This article investigates a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier training. Our framework parses a target image wit…
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection
Xiaolong Wang, Liang Lin, Lichao Huang +1
This paper proposes a reconfigurable model to recognize and detect multiclass (or multiview) objects with large variation in appearance. Compared with well acknowledged hierarchica…
Deep Joint Task Learning for Generic Object Extraction
Xiaolong Wang, Liliang Zhang, Liang Lin +2
This paper investigates how to extract objects-of-interest without relying on hand-craft features and sliding windows approaches, that aims to jointly solve two sub-tasks: (i) rapi…
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection
Xiaolong Wang, Liang Lin
This paper studies a novel discriminative part-based model to represent and recognize object shapes with an "And-Or graph". We define this model consisting of three layers: the lea…