Human-object interaction(HOIs)detection is a new branch of visual relationship detection,which plays an important role in the field of image understanding.Because of the complexity and diversity of image content,the d...Human-object interaction(HOIs)detection is a new branch of visual relationship detection,which plays an important role in the field of image understanding.Because of the complexity and diversity of image content,the detection of HOIs is still an onerous challenge.Unlike most of the current works for HOIs detection which only rely on the pairwise information of a human and an object,we propose a graph-based HOIs detection method that models context and global structure information.Firstly,to better utilize the relations between humans and objects,the detected humans and objects are regarded as nodes to construct a fully connected undirected graph,and the graph is pruned to obtain an HOI graph that only preserving the edges connecting human and object nodes.Then,in order to obtain more robust features of human and object nodes,two different attention-based feature extraction networks are proposed,which model global and local contexts respectively.Finally,the graph attention network is introduced to pass messages between different nodes in the HOI graph iteratively,and detect the potential HOIs.Experiments on V-COCO and HICO-DET datasets verify the effectiveness of the proposed method,and show that it is superior to many existing methods.展开更多
This letter presents a face normalization algorithm based on 2-D face model to recognize faces with variant postures from front-view face. A 2-D face mesh model can be extracted from faces with rotation to left or rig...This letter presents a face normalization algorithm based on 2-D face model to recognize faces with variant postures from front-view face. A 2-D face mesh model can be extracted from faces with rotation to left or right and the corresponding front-view mesh model can be estimated according to the facial symmetry. Then based on the inner relationship between the two mesh models, the normalized front-view face is formed by gray level mapping. Finally, the face recognition will be finished based on Principal Component Analysis (PCA). Experiments show that better face recognition performance is achieved in this way.展开更多
The rate and distortion of Id-slice do not fit the globally linear relationship on a logarithmic scale. Lagrange multiplier selection methods based on the globally linear approximate relationship are neither efficient...The rate and distortion of Id-slice do not fit the globally linear relationship on a logarithmic scale. Lagrange multiplier selection methods based on the globally linear approximate relationship are neither efficient nor optimal for multi-view video coding (MVC). To improve the coding efficiency of MVC, a local curve fitting based Lagrange multiplier selection method is proposed in this paper, where Lagrange multipliers are selected according to the local slopes of the approximate curves. Experi-mental results showed that the proposed method improves the coding efficiency. Up to 2.5 dB gain was achieved at low bitrates.展开更多
基金Project(51678075)supported by the National Natural Science Foundation of ChinaProject(2017GK2271)supported by the Hunan Provincial Science and Technology Department,China。
文摘Human-object interaction(HOIs)detection is a new branch of visual relationship detection,which plays an important role in the field of image understanding.Because of the complexity and diversity of image content,the detection of HOIs is still an onerous challenge.Unlike most of the current works for HOIs detection which only rely on the pairwise information of a human and an object,we propose a graph-based HOIs detection method that models context and global structure information.Firstly,to better utilize the relations between humans and objects,the detected humans and objects are regarded as nodes to construct a fully connected undirected graph,and the graph is pruned to obtain an HOI graph that only preserving the edges connecting human and object nodes.Then,in order to obtain more robust features of human and object nodes,two different attention-based feature extraction networks are proposed,which model global and local contexts respectively.Finally,the graph attention network is introduced to pass messages between different nodes in the HOI graph iteratively,and detect the potential HOIs.Experiments on V-COCO and HICO-DET datasets verify the effectiveness of the proposed method,and show that it is superior to many existing methods.
基金Supported by the National 863 Project(2001AA114140)and NNSF of China (90104013)
文摘This letter presents a face normalization algorithm based on 2-D face model to recognize faces with variant postures from front-view face. A 2-D face mesh model can be extracted from faces with rotation to left or right and the corresponding front-view mesh model can be estimated according to the facial symmetry. Then based on the inner relationship between the two mesh models, the normalized front-view face is formed by gray level mapping. Finally, the face recognition will be finished based on Principal Component Analysis (PCA). Experiments show that better face recognition performance is achieved in this way.
基金Project (Nos. 60505017 and 60534070) supported by the National Natural Science Foundation of China
文摘The rate and distortion of Id-slice do not fit the globally linear relationship on a logarithmic scale. Lagrange multiplier selection methods based on the globally linear approximate relationship are neither efficient nor optimal for multi-view video coding (MVC). To improve the coding efficiency of MVC, a local curve fitting based Lagrange multiplier selection method is proposed in this paper, where Lagrange multipliers are selected according to the local slopes of the approximate curves. Experi-mental results showed that the proposed method improves the coding efficiency. Up to 2.5 dB gain was achieved at low bitrates.
