Search Results for author:
Found 97 papers, 41 papers with code
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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 #39 on
Skeleton Based Action Recognition
on NTU RGB+D
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.
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.
3D Point Cloud Processing and Learning for Autonomous Driving
We present a review of 3D point cloud processing and learning for autonomous driving.
Collaborative Motion Prediction via Neural Motion Message Passing
Motion prediction is essential and challenging for autonomous vehicles and social robots.
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.
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.
Pruned Graph Scattering Transforms
Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks.
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.
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.
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.
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.
Learning on Attribute-Missing Graphs
Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community.
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.
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.
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.
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.
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).
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 #7 on
Weakly Supervised Action Localization
on THUMOS14
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
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.
Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning
Experiments are conducted on FPHA and HO-3D datasets.
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.
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.
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.
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 #251 on
3D Human Pose Estimation
on Human3.6M
Learning to Learn Graph Topologies
The model is trained in an end-to-end fashion with pairs of node data and graph samples.
Detecting Backdoor Attacks Against Point Cloud Classifiers
Backdoor attacks (BA) are an emerging threat to deep neural network classifiers.
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.
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.
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.
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.
Ranked #4 on
3D Object Detection
on V2XSet
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.
Ranked #4 on
Point Cloud Quality Assessment
on WPC
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.
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.
V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving
Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving.
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 #5 on
Trajectory Prediction
on Stanford Drone
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.
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).
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.
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Further, we apply the proposed framework to current SOTA multi-agent multi-modal forecasting systems as a plugin module, which enables the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal trajectory forecasting task; 2) rank the multiple predictions and select the optimal one based on the estimated uncertainty.
Latency-Aware Collaborative Perception
Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception.
Aware of the History: Trajectory Forecasting with the Local Behavior Data
In this work, we re-introduce this information as a new type of input data for trajectory forecasting systems: the local behavior data, which we conceptualize as a collection of location-specific historical trajectories.
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
To address the first issue, we propose adaptive graph scattering, which leverages multiple trainable band-pass graph filters to decompose pose features into richer graph spectrum bands.
Weakly Supervised Online Action Detection for Infant General Movements
Although general movements assessment(GMA) has shown promising results in early CP detection, it is laborious.
Aerial Monocular 3D Object Detection
To fill the gap, this work proposes a dual-view detection system named DVDET to achieve aerial monocular object detection in both the 2D image space and the 3D physical space.
Neural Message Passing for Visual Relationship Detection
Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and interactions.
Collaborative Perception for Autonomous Driving: Current Status and Future Trend
Perception is one of the crucial module of the autonomous driving system, which has made great progress recently.
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication.
Ranked #1 on
Monocular 3D Object Detection
on CoPerception-UAVs
FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning
This method attempts to mitigate the negative effects of data heterogeneity in FL by aligning each client's feature space.
Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation
Instead of learning a single prototype for each class, in this paper, we propose to use an adaptive number of prototypes to dynamically describe the different point patterns within a semantic class.
Ranked #17 on
3D Semantic Segmentation
on SemanticKITTI
Unrolled Graph Learning for Multi-Agent Collaboration
To fill this gap, we propose a distributed multi-agent learning model inspired by human collaboration, in which the agents can autonomously detect suitable collaborators and refer to collaborators' model for better performance.
Learning Hypergraphs From Signals With Dual Smoothness Prior
Hypergraph structure learning, which aims to learn the hypergraph structures from the observed signals to capture the intrinsic high-order relationships among the entities, becomes crucial when a hypergraph topology is not readily available in the datasets.
Robust Collaborative 3D Object Detection in Presence of Pose Errors
Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion.
Discriminative Radial Domain Adaptation
Domain adaptation methods reduce domain shift typically by learning domain-invariant features.
Among Us: Adversarially Robust Collaborative Perception by Consensus
This leads to our hypothesize-and-verify framework: perception results with and without collaboration from a random subset of teammates are compared until reaching a consensus.
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving
Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial-temporal priors.
Ranked #2 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU ped - 224x480 - Vis filter. - 100x100 at 0.5 metric)
Leapfrog Diffusion Model for Stochastic Trajectory Prediction
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.
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle.
Ranked #1 on
Human Pose Forecasting
on Human3.6M
Collaboration Helps Camera Overtake LiDAR in 3D Detection
Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems.
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving
To achieve comprehensive recovery, we design a communication-adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information.
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
However, based on our empirical observations and theoretical analysis, we find that the dataset size is not optimal and the discrepancy between local and global category distributions could be a beneficial and complementary indicator for determining aggregation weights.
Joint-Relation Transformer for Multi-Person Motion Prediction
Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people.
Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
To work with auxiliary tasks, we propose a novel auxiliary-adapted transformer, which can handle incomplete, corrupted motion data and achieve coordinate recovery via capturing spatial-temporal dependencies.
Hypergraph Structure Inference From Data Under Smoothness Prior
However, existing methods either adopt simple pre-defined rules that fail to precisely capture the distribution of the potential hypergraph structure, or learn a mapping between hypergraph structures and node features but require a large amount of labelled data, i. e., pre-existing hypergraph structures, for training.
Compatible Transformer for Irregularly Sampled Multivariate Time Series
To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples remain unchanged.
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation
FedCOG consists of two key components at the client side: complementary data generation, which generates data extracted from the shared global model to complement the original dataset, and knowledge-distillation-based model training, which distills knowledge from global model to local model based on the generated data to mitigate over-fitting the original heterogeneous dataset.
Federated Learning Empowered by Generative Content
In this paper, we propose a novel FL framework termed FedGC, designed to mitigate data heterogeneity issues by diversifying private data with generative content.
Hypergraph-MLP: Learning on Hypergraphs without Message Passing
Hypergraphs are vital in modelling data with higher-order relations containing more than two entities, gaining prominence in machine learning and signal processing.
Hypergraph Transformer for Semi-Supervised Classification
Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities.
Learn What You Need in Personalized Federated Learning
Personalized federated learning aims to address data heterogeneity across local clients in federated learning.
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving.
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
In this paper, we introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation.
Pragmatic Communication in Multi-Agent Collaborative Perception
Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration.
An Extensible Framework for Open Heterogeneous Collaborative Perception
In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception, while ensuring high perception performance and low integration cost?
Hypergraph Node Classification With Graph Neural Networks
However, in this paper, we theoretically demonstrate that, in the context of node classification, most HyperGNNs can be approximated using a GNN with a weighted clique expansion of the hypergraph.
Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
Aligning large language models (LLMs) with human values is imperative to mitigate potential adverse effects resulting from their misuse.
Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents
Furthermore, to unleash the potential of extensive high-quality digital assets, ChatSim employs a novel multi-camera lighting estimation method to achieve scene-consistent assets' rendering.
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields.
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research.
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
In this paper, we propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
FastMAC: Stochastic Spectral Sampling of Correspondence Graph
As such, the core of our method is the stochastic spectral sampling of correspondence graph.
V2X-PC: Vehicle-to-everything Collaborative Perception via Point Cluster
The point cluster inherently preserves object information while packing messages, with weak relevance to the collaboration range, and supports explicit structure modeling.
Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
This effort necessitates two key foundations: a platform capable of generating data to facilitate the training and testing of V2X-AD, and a comprehensive system that integrates full driving-related functionalities with mechanisms for information sharing.
Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models
The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors.
