Suping Wu's research while affiliated with Ningxia University and other places
What is this page?
This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
Publications (17)
Single-view 3D object reconstruction has always been a long-term challenging task. The objects with complex topologies are hard to accurately be reconstructed, which makes the existing methods suffer from the blurring of the shape boundary between the multiple components in the object. Moreover, most of them cannot balance the learning between glob...
In this paper, we present a spatio-temporal tendency reasoning (STR) network for recovering human body pose and shape from videos. Previous approaches have focused on how to extend 3D human datasets and temporal-based learning to promote accuracy and temporal smoothing. Different from them, our STR aims to learn accurate and natural motion sequence...
In the unconstrained scene, a large amount of self-occlusion and large poses occurs in the face. These occluded points lead to a huge challenge in face alignment and reconstruction. When performing 3DMM parameter regression, existing face alignment and reconstruction methods only consider reducing the error between the predicted point and the real...
In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making full access to the limited training samples which likely leads to bias age prediction, our SADAL aims to seek batches of unobserved hard-negative samples based on existing training samples, which typically reinforces...
Plant disease detection plays an important role in agricultural production and ecological protection. However, it is always a challenge to detect the severity of plant diseases in multi-species and multi-disease conditions. Unlike most existing classification methods which are difficult to solve multi-properties detection, we propose a disentangled...
Unconstrained face alignment usually undergoes extreme deformations and severe occlusions, which likely gives rise to biased shape prediction. Most existing methods simply exploit shape structure by directly concatenating all landmarks, which leads to losses of facial details in extreme deformation regions. In this paper, we propose a relational-st...
In this paper, we propose an end-to-end reasoning-decision networks (RDN) approach for robust face alignment via policy gradient. Unlike the conventional coarse-to-fine approaches which likely lead to bias prediction due to poor initialization, our approach aims to learn a policy by leveraging raw pixels to reason a subset of shape candidates, sequ...
Citations
... Both of them depend too much on hard-examples mining because most image pairs sampled by the original random sampling strategy satisfy loss-constrained conditions during the late stage of training and make a few contributions to further learning. We do not mine hard-examples in experiments of adding contrastive and triplet loss, which leads to the repeated training process and difficult convergence of the network when optimizing the network by contrastive loss [35] and triplet loss [55]. Therefore, the improvement is slight. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/851498636423168-1580024503219_Q64/Hao-Liu-4.jpg)
... Inspired by the reinforcement learning for face-alignment tasks, in this work we propose medical landmark detection as a Markov decision process [49]. We define L = [L 1 , L 2 , · · · , L I ] ∈ R 2×I as a location vector of I points, where L i denotes for the horizontal and vertical coordinates of the i-th landmark, given a medical image I. ...
