79 research outputs found
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
We propose a novel method based on teacher-student learning framework for 3D
human pose estimation without any 3D annotation or side information. To solve
this unsupervised-learning problem, the teacher network adopts
pose-dictionary-based modeling for regularization to estimate a physically
plausible 3D pose. To handle the decomposition ambiguity in the teacher
network, we propose a cycle-consistent architecture promoting a 3D
rotation-invariant property to train the teacher network. To further improve
the estimation accuracy, the student network adopts a novel graph convolution
network for flexibility to directly estimate the 3D coordinates. Another
cycle-consistent architecture promoting 3D rotation-equivariant property is
adopted to exploit geometry consistency, together with knowledge distillation
from the teacher network to improve the pose estimation performance. We conduct
extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D
joint prediction error by 11.4% compared to state-of-the-art unsupervised
methods and also outperforms many weakly-supervised methods that use side
information on Human3.6M. Code will be available at
https://github.com/sjtuxcx/ITES.Comment: Accepted in AAAI 202
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
Zero-shot entity retrieval, aiming to link mentions to candidate entities
under the zero-shot setting, is vital for many tasks in Natural Language
Processing. Most existing methods represent mentions/entities via the sentence
embeddings of corresponding context from the Pre-trained Language Model.
However, we argue that such coarse-grained sentence embeddings can not fully
model the mentions/entities, especially when the attention scores towards
mentions/entities are relatively low. In this work, we propose GER, a
\textbf{G}raph enhanced \textbf{E}ntity \textbf{R}etrieval framework, to
capture more fine-grained information as complementary to sentence embeddings.
We extract the knowledge units from the corresponding context and then
construct a mention/entity centralized graph. Hence, we can learn the
fine-grained information about mention/entity by aggregating information from
these knowledge units. To avoid the graph information bottleneck for the
central mention/entity node, we construct a hierarchical graph and design a
novel Hierarchical Graph Attention Network~(HGAN). Experimental results on
popular benchmarks demonstrate that our proposed GER framework performs better
than previous state-of-the-art models. The code has been available at
https://github.com/wutaiqiang/GER-WSDM2023.Comment: 9 pages, 5 figure
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
Multivariate time series forecasting has long received significant attention
in real-world applications, such as energy consumption and traffic prediction.
While recent methods demonstrate good forecasting abilities, they have three
fundamental limitations. (i) Discrete neural architectures: Interlacing
individually parameterized spatial and temporal blocks to encode rich
underlying patterns leads to discontinuous latent state trajectories and higher
forecasting numerical errors. (ii) High complexity: Discrete approaches
complicate models with dedicated designs and redundant parameters, leading to
higher computational and memory overheads. (iii) Reliance on graph priors:
Relying on predefined static graph structures limits their effectiveness and
practicability in real-world applications. In this paper, we address all the
above limitations by proposing a continuous model to forecast
ultivariate ime series with dynamic raph
neural rdinary ifferential quations
(). Specifically, we first abstract multivariate time series
into dynamic graphs with time-evolving node features and unknown graph
structures. Then, we design and solve a neural ODE to complement missing graph
topologies and unify both spatial and temporal message passing, allowing deeper
graph propagation and fine-grained temporal information aggregation to
characterize stable and precise latent spatial-temporal dynamics. Our
experiments demonstrate the superiorities of from various
perspectives on five time series benchmark datasets.Comment: 14 pages, 6 figures, 5 table
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