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Graph Convolutional Networks
Graph convolutional networks (GCNs) have harvested considerable attention in the learning representation field, generalizing the power of CNNs to processing data organized in graph structure (e.g., citation networks, 3D meshes). However, according to the challenges listed below, we follow our research directions towards:
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GCNs suffer from the over-smoothing problem. The work of Hoang et al. blame this to the constant low-pass filtering. We further generalize this challenge to achieving full filter representation power. And we show that merely adopting second-order polynomial filters can overcome filtering deficiency, contrasting to the one-hop GCNs (Kipf et al.) and ad hoc tricks in GCNII (Chen et al.).
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GCNs cannot go deeper even with versatile layers on the spectral domain. Our latest progress proves that arbitrary graph convolutional layer (as a polynomial in adjacency matrix) suffer from over-smoothing problem under a certain condition. The culprits include both the linear convolution and the ReLU activation. Therefore, we are looking into the underlying graph structures, optimization/training schemes, and the nonlinearity to root up over-smoothing from GCNs.
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Message-passing GNNs (MPNNs) are another popular branch of graph representation learning. However, they incorporate more agnostic mechanisms, thereby hindering extensive theoretical studies on them. The lastest progress is made by Xu et al. Empolying their WL-test framework, we are able to investigate MPNNs' dicriminativity, generalizability, and stability.
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Neural Representation and Optimization
Multi-layer perceptron (MLP), proved to be a universal approximator, has the potential to encode every digital object as an implicit function. For example, images, 3D surface/volumes, and human avatars. This latent representation can empower reconstruction algorithms with a differential forward model and stochastic gradient descent optimization procedures, such as Neural Radiance Field (NeRF), a successful instance of this idea. Our research lies in a broader field of neural representation and reconstruction in computer graphics, computational imaging, and bioinformatics. Specifically, we explore the possibility of applications in Electron Transmission Microscopy (TEM), Non-Line-Of-Sight imaging (NLOS), and depth from defocus.
Moreover, we are studying how to interact with the implicit representation, in which we can impose more structural correlations and constraints to facilitate the reconstruction. As we refer to MLP as a universal interpolator, we are doing theoretical research on the working mechanism behind the representation capacity of MLP.
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3D Pose Estimation and Understanding
Complex poses, data annotation, and motion-blur still remain as challenges for 3D pose estimation. We observed that current learning-based models significantly suffer from the domain gap between both human motions (e.g., competitive diving vs. walking) and the surrounding scenes (e.g., indoor vs. in-the-wild). Specifically, a pose estimator trained on dedicated datasets (e.g., Diving48) takes advantages in both accuracy and efficiency. However, those datasets with high-quality 3D annotations for challenging poses (e.g., competitive sports) are usually hard to acquire. A possible solution by exploiting synthetic data, however, cannot reach comparable performance. To this end, we intend to employ semi-supervised inverse rendering to extract pose and appearance representations separately, and borrow content-based domain adaptation to eliminate the domain gaps between virtual data and real-captured data on these two representation domains, respectively. So that we can obtain large-scale datasets with realistic human images and the autonomously annotated groundtruth.
Keep thy heart with all diligence; for out of it are the issues of life.
I was born and raised up in Chengdu, southwestern China, blessed with great influence in science and technology by my father. I taught myself C/C++ programming languages at 12 years old, and then became a "teenager developer" since high school.
I came to Shanghai, eastern China to pursue my Bachelor's degree at ShanghaiTech University with a focus on machine learning and computer vision. I enjoy programming and reading insightful papers. I believe good work should deliver either mathematical motivations or extensive engineering efforts. Besides laboratory, basketball and guitar compose my life.
