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Works226 from public data
- Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition1,229
The proposed AS-GCN achieves consistently large improvement compared to the state-of-the-art methods and shows promising results for future pose prediction.
- Discrete Signal Processing on Graphs: Sampling Theory692
It is shown that perfect recovery is possible for graph signals bandlimited under the graph Fourier transform and the connection to the sampling theory of finite discrete-time signal processing and previous work on signal recovery on graphs is established.
- V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving419
V2X-Sim seeks to stimulate collaborative perception research for autonomous driving before realistic datasets become widely available and builds an open-source testbed and provides a benchmark for the state-of-the-art collaborative perception algorithms on three tasks, including detection, tracking and segmentation.
- Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction374
Novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions and outperforms state-of-the-art methods in both short and long-term predictions on the datasets of Human 3.6M and CMU Mocap.
- Synthesis of a mixed valence state Ce-MOF as an oxidase mimetic for the colorimetric detection of biothiols360
A facile and rapid in situ partial oxidation synthetic strategy for the fabrication of a mixed valence state Ce-MOF (MVCM) which exhibits intrinsic oxidase-like activity is demonstrated and a colorimetric approach for the high-throughput detection of biothiols in serum samples was established.
- Signal Recovery on Graphs: Variation Minimization305
This work formulate graph signal recovery as an optimization problem, for which it provides a general solution through the alternating direction methods of multipliers through which it relates to signal inpainting, matrix completion, robust principal component analysis, and anomaly detection.
- Signal Recovery on Graphs305
This work formulate graph signal recovery as an optimization problem, for which it provides a general solution through the alternating direction methods of multipliers through which it relates to signal inpainting, matrix completion, robust principal component analysis, and anomaly detection.
- Leapfrog Diffusion Model for Stochastic Trajectory Prediction286
The core of the proposed LED is to leverage a trainable leapfrog initializer to directly learn an expressive multi-modal distribution of future trajectories, which skips a large number of denoising steps, significantly accelerating inference speed.
- GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning281
GroupNet, a multiscale hypergraph neural network, is proposed, which is novel in terms of both interaction capturing and representation learning and applies into both CVAE-based prediction system and previous state-of-the-art prediction systems for predicting socially plausible trajectories with relational reasoning.
- 3D Point Cloud Processing and Learning for Autonomous Driving: Impacting Map Creation, Localization, and Perception258
The recent progress in this research area is reviewed and what has been tried and what is needed for practical and safe AVs are summarized.
- Robust Collaborative 3D Object Detection in Presence of Pose Errors247
CoAlign is a novel hybrid collaboration framework that is robust to unknown pose errors and relies on a novel agent-object pose graph modeling to enhance pose consistency among collaborating agents and adopts a multiscale data fusion strategy to aggregate intermediate features at multiple spatial resolutions.
- Symbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction240
This work proposes a symbiotic model to handle two tasks jointly, and proposes two scales of graphs to explicitly capture relations among body-joints and body-parts, and shows that the symbiotic graph neural networks achieve better performances on both tasks compared to the state-of-the-art methods.
- Cell clustering for spatial transcriptomics data with graph neural networks229
It is shown that cell clustering for spatial transcripts outperforms other spatial clustering approaches on spatial transcriptomics datasets and can clearly identify all four cell cycle phases from multiplexed error-robust fluorescence in situ hybridization data of cultured cells.
- Remember Intentions: Retrospective-Memory-based Trajectory Prediction218
This work designs a pair of memory banks to explicitly store representative instances in the training set, acting as prefrontal cortex in the neural system, and proposes MemoNet, an instance-based approach that predicts the movement intentions of agents by looking for similar scenarios in theTraining data.
- EqMotion: Equivariant Multi-Agent Motion Prediction with Invariant Interaction Reasoning212
This work proposes Eq-Motion, an efficient equivariant motion prediction model with invariant interaction reasoning module, which achieves state-of-the-art prediction performances on all the four tasks, improving by 24.0/30.1/8.6/9.2%.
- Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs206
A continuous model to forecast multivariate time series forecasting is proposed, designed and solved 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.
- MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps197
Extensive experiments show that the proposed method overall outperforms the state-of-the-arts, including the latest scene-flow- and 3D-object-detection-based methods, indicating the potential value of the proposed methods serving as a backup to the bounding-box-based system, and providing complementary information to the motion planner in autonomous driving.
- Fast Resampling of Three-Dimensional Point Clouds via Graphs182
This work uses a general feature-extraction operator to represent application-dependent features and proposes a general reconstruction error to evaluate the quality of resampling; by minimizing the error, it obtains a general form of optimal resamplings distribution.
- Signal denoising on graphs via graph filtering173
This paper forms graph signal denoising as an optimization problem and derives an exact closed-form solution expressed by an inverse graph filter, as well as an approximate iterative solution express by a standard graph filter.
- FedDisco: Federated Learning with Discrepancy-Aware Collaboration164
This work proposes a novel aggregation method, Federated Learning with Discrepancy-aware Collaboration (FedDisco), whose aggregation weights not only involve both the dataset size and the discrepancy value, but also contribute to a tighter theoretical upper bound of the optimization error.
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- An Extensible Framework for Open Heterogeneous Collaborative Perception159
Heterogeneous ALliance (HEAL), a novel extensible collaborative perception framework that surpasses SOTA methods in performance while reducing the training parameters by 91.5% when integrating 3 new agent types.
- Collaboration Helps Camera Overtake LiDAR in 3D Detection159
The proposed collaborative camera-only 3D detection (CoCa3D) enables agents to share complementary information with each other through communication, and shows a potential that with sufficient collaboration, the camera might overtake LiDAR in some practical scenarios.
- Learning on Attribute-Missing Graphs149
This article makes a shared-latent space assumption on graphs and develops a novel distribution matching-based GNN called structure-attribute transformer (SAT) for attribute-missing graphs that shows better performance than other methods on both link prediction and node attribute completion tasks.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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