676 citations · 4.7k across the 170 of their papers we have counts for
27 papers · 2 filters
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Tianshui Chen, Liang Lin, Lingbo Liu +2
Salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. Although a variety of powerful…
Reversible Recursive Instance-level Object Segmentation
Xiaodan Liang, Yunchao Wei, Xiaohui Shen +4
In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-I…
Semantic Object Parsing with Local-Global Long Short-Term Memory
Xiaodan Liang, Xiaohui Shen, Donglai Xiang +3
Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical f…
Proposal-free Network for Instance-level Object Segmentation
Xiaodan Liang, Yunchao Wei, Xiaohui Shen +3
Instance-level object segmentation is an important yet under-explored task. The few existing studies are almost all based on region proposal methods to extract candidate segments a…
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy
Zhanglin Peng, Ya Li, Zhaoquan Cai +1
This work investigates how the traditional image classification pipelines can be extended into a deep architecture, inspired by recent successes of deep neural networks. We propose…
Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional Networks
Zhujin Liang, Shengyong Ding, Liang Lin
This paper investigates how to rapidly and accurately localize facial landmarks in unconstrained, cluttered environments rather than in the well segmented face images. We present a…
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence
Keze Wang, Liang Lin, Jiangbo Lu +2
Driven by recent vision and graphics applications such as image segmentation and object recognition, computing pixel-accurate saliency values to uniformly highlight foreground obje…
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…
Deep Human Parsing with Active Template Regression
Xiaodan Liang, Si Liu, Xiaohui Shen +5
In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an Active Template Regression (ATR) problem, where the n…
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…
Clothing Co-Parsing by Joint Image Segmentation and Labeling
Wei Yang, Ping Luo, Liang Lin
This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic co…
Learning Contour-Fragment-based Shape Model with And-Or Tree Representation
Liang Lin, Xiaolong Wang, Wei Yang +1
This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the f…
Deep Boosting: Layered Feature Mining for General Image Classification
Zhanglin Peng, Liang Lin, Ruimao Zhang +1
Constructing effective representations is a critical but challenging problem in multimedia understanding. The traditional handcraft features often rely on domain knowledge, limitin…
An Expressive Deep Model for Human Action Parsing from A Single Image
Zhujin Liang, Xiaolong Wang, Rui Huang +1
This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images. Its main challenges lie in the large variations in human p…
Towards a solid solution of real-time fire and flame detection
Bo Jiang, Yongyi Lu, Xiying Li +1
Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored. This paper presents an empiri…
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance
Liang Lin, Yongyi Lu, Yan Pan +1
In order to track the moving objects in long range against occlusion, interruption, and background clutter, this paper proposes a unified approach for global trajectory analysis. I…
Adaptive Scene Category Discovery with Generative Learning and Compositional Sampling
Liang Lin, Ruimao Zhang, Xiaohua Duan
This paper investigates a general framework to discover categories of unlabeled scene images according to their appearances (i.e., textures and structures). We jointly solve the tw…
Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models
Liang Lin, Yuanlu Xu, Xiaodan Liang +1
Although it has been widely discussed in video surveillance, background subtraction is still an open problem in the context of complex scenarios, e.g., dynamic backgrounds, illumin…
Discriminatively Trained And-Or Graph Models for Object Shape Detection
Liang Lin, Xiaolong Wang, Wei Yang +1
In this paper, we investigate a novel reconfigurable part-based model, namely And-Or graph model, to recognize object shapes in images. Our proposed model consists of four layers:…
Learning Latent Spatio-Temporal Compositional Model for Human Action Recognition
Xiaodan Liang, Liang Lin, Liangliang Cao
Action recognition is an important problem in multimedia understanding. This paper addresses this problem by building an expressive compositional action model. We model one action…