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Found 52 papers, 17 papers with code
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction.
Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling
Specifically, adaptive spherical wavelets are learned with a lifting structure that consists of trainable lifting operators (i. e., update and predict operators).
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning
From the aspect of interaction capturing, we propose a trainable multiscale hypergraph to capture both pair-wise and group-wise interactions at multiple group sizes.
Remember Intentions: Retrospective-Memory-based Trajectory Prediction
However, in this way, the model parameters come from all seen instances, which means a huge amount of irrelevant seen instances might also involve in predicting the current situation, disturbing the performance.
Ranked #2 on
Trajectory Prediction
on Stanford Drone
V2X-Sim: A Virtual Collaborative Perception Dataset for Autonomous Driving
Vehicle-to-everything (V2X), which denotes the collaboration between a vehicle and any entity in its surrounding, can fundamentally improve the perception in self-driving systems.
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.
Task Decoupled Framework for Reference-Based Super-Resolution
Those methods conduct the super-resolution task of the input low-resolution(LR) image and the texture transfer task from the reference image together in one module, easily introducing the interference between LR and reference features.
No-Reference Point Cloud Quality Assessment via Domain Adaptation
We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds.
Learning Distilled Collaboration Graph for Multi-Agent Perception
Our approach is validated on V2X-Sim 1. 0, a large-scale multi-agent perception dataset that we synthesized using CARLA and SUMO co-simulation.
Dynamic Differential-Privacy Preserving SGD
The equivalent privacy costs controlled by maintaining the same gradient clipping thresholds and noise powers in each step result in unstable updates and a lower model accuracy when compared to the non-DP counterpart.
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances.
Spatio-Temporal Graph Complementary Scattering Networks
Spatio-temporal graph signal analysis has a significant impact on a wide range of applications, including hand/body pose action recognition.
Detecting Backdoor Attacks Against Point Cloud Classifiers
Backdoor attacks (BA) are an emerging threat to deep neural network classifiers.
Learning to Learn Graph Topologies
The model is trained in an end-to-end fashion with pairs of node data and graph samples.
Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer Graph
We use a coarse graph, derived from a dense graph, to estimate the human's 3D pose, and the dense graph to estimate the 3D shape.
Ranked #195 on
3D Human Pose Estimation
on Human3.6M
A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer
To leverage the human body shape prior, LPNet exploits the topological information of the body mesh to learn an expressive visual representation for the target person in the 3D mesh space.
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction
The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the relations in motions at various spatial and temporal scales.
CaT: Weakly Supervised Object Detection with Category Transfer
To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets.
Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning
Experiments are conducted on FPHA and HO-3D datasets.
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system.
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
To solve this issue, we introduce an adaptive mutual supervision framework (AMS) with two branches, where the base branch adopts CAS to localize the most discriminative action regions, while the supplementary branch localizes the less discriminative action regions through a novel adaptive sampler.
Ranked #4 on
Weakly Supervised Action Localization
on THUMOS14
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy
In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED).
Divide and Conquer for Single-Frame Temporal Action Localization
Single-frame temporal action localization (STAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance.
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
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.
Spatio-Temporal Graph Scattering Transform
Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data.
Sampling and Recovery of Graph Signals based on Graph Neural Networks
Compared to previous analytical sampling and recovery, the proposed methods are able to flexibly learn a variety of graph signal models from data by leveraging the learning ability of neural networks; compared to previous neural-network-based sampling and recovery, the proposed methods are designed through exploiting specific graph properties and provide interpretability.
Learning on Attribute-Missing Graphs
Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community.
Graph Cross Networks with Vertex Infomax Pooling
Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermediate features across scales to promote information flow.
Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs
The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs.
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph
Most existing methods focus on preserving the first-order proximity between entities in the KG.
Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
To prevent the cliff effect subject to channel quality fluctuation, we have proposed soft point cloud delivery called HoloCast.
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising
We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals.
Pruned Graph Scattering Transforms
Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks.
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning.
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps
The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion.
Collaborative Motion Prediction via Neural Motion Message Passing
Motion prediction is essential and challenging for autonomous vehicles and social robots.
3D Point Cloud Processing and Learning for Autonomous Driving
We present a review of 3D point cloud processing and learning for autonomous driving.
Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring
We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health.
Efficient and Stable Graph Scattering Transforms via Pruning
The resultant pruning algorithm is guided by a graph-spectrum-inspired criterion, and retains informative scattering features on-the-fly while bypassing the exponential complexity associated with GSTs.
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction
For the backbone, we propose multi-branch multi-scale graph convolution networks to extract spatial and temporal features.
Ranked #22 on
Skeleton Based Action Recognition
on NTU RGB+D
Node Attribute Generation on Graphs
NANG learns a unifying latent representation which is shared by both node attributes and graph structures and can be translated to different modalities.
Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving
We present a novel graph-neural-network-based system to effectively represent large-scale 3D point clouds with the applications to autonomous driving.
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
The experimental results show that (1) the proposed networks outperform the state-of-the-art methods in various tasks; (2) a graph topology can be inferred as auxiliary information without specific supervision on graph topology inference; and (3) graph filtering refines the reconstruction, leading to better performances.
Handwritten Chinese Font Generation with Collaborative Stroke Refinement
Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters.
Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics.
3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation
NPD algorithm uses a neural network to estimate reference planes for points in noisy point clouds.
Generalized Value Iteration Networks: Life Beyond Lattices
GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs.
Fast Resampling of 3D Point Clouds via Graphs
We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling.
Signal Representations on Graphs: Tools and Applications
For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties.
A statistical perspective of sampling scores for linear regression
In this paper, we consider a statistical problem of learning a linear model from noisy samples.
Signal Recovery on Graphs: Random versus Experimentally Designed Sampling
We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling.
Signal Recovery on Graphs: Variation Minimization
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph.
