Search Results for author:
Found 24 papers, 11 papers with code
Urban Architect: Steerable 3D Urban Scene Generation with Layout Prior
(2) To handle the unbounded nature of urban scenes, we represent 3D scene with a Scalable Hash Grid structure, incrementally adapting to the growing scale of urban scenes.
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
In light of this, we propose LiDAR4D, a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis.
DreamLIP: Language-Image Pre-training with Long Captions
Motivated by this, we propose to dynamically sample sub-captions from the text label to construct multiple positive pairs, and introduce a grouping loss to match the embeddings of each sub-caption with its corresponding local image patches in a self-supervised manner.
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds
Therefore, existing complementary learning approaches for MDE fuse intensity information from images and scene details from event data for better scene understanding.
Joint Extraction of Uyghur Medicine Knowledge with Edge Computing
To address these challenges, a joint extraction model with parameter sharing in edge computing is proposed, named CoEx-Bert.
Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
Firstly, we introduce a lightweight distributed knowledge graph completion architecture that utilizes knowledge graph embedding for data analysis.
Likelihood-Aware Semantic Alignment for Full-Spectrum Out-of-Distribution Detection
Full-spectrum out-of-distribution (F-OOD) detection aims to accurately recognize in-distribution (ID) samples while encountering semantic and covariate shifts simultaneously.
HDMNet: A Hierarchical Matching Network with Double Attention for Large-scale Outdoor LiDAR Point Cloud Registration
Specifically, A novel feature consistency enhanced double-soft matching network is introduced to achieve two-stage matching with high flexibility while enlarging the receptive field with high efficiency in a patch-to patch manner, which significantly improves the registration performance.
Convex Q Learning in a Stochastic Environment: Extended Version
The main contributions firstly concern properties of the relaxation, described as a deterministic convex program: we identify conditions for a bounded solution, and a significant relationship between the solution to the new convex program, and the solution to standard Q-learning.
Urban Radiance Field Representation with Deformable Neural Mesh Primitives
To construct urban-level radiance fields efficiently, we design Deformable Neural Mesh Primitive~(DNMP), and propose to parameterize the entire scene with such primitives.
NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation
In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios.
Ranked #1 on
3D Point Cloud Interpolation
on NL-Drive
Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection
Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set.
Sufficient Exploration for Convex Q-learning
The main contributions follow: (i) The dual of convex Q-learning is not precisely Manne's LP or a version of logistic Q-learning, but has similar structure that reveals the need for regularization to avoid over-fitting.
Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time
Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure.
Modeling User Behavior with Graph Convolution for Personalized Product Search
Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences.
HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet.
ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization
Traditional methods mainly focus on foreground and background frames separation with only a single attention branch and class activation sequence.
Ranked #5 on
Weakly Supervised Action Localization
on THUMOS14
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
PointINet: Point Cloud Frame Interpolation Network
Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.
MoNet: Motion-based Point Cloud Prediction Network
3D point clouds accurately model 3D information of surrounding environment and are crucial for intelligent vehicles to perceive the scene.
LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection
At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features.
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points.
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling
Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system.
Zap Q-Learning With Nonlinear Function Approximation
Based on multiple experiments with a range of neural network sizes, it is found that the new algorithms converge quickly and are robust to choice of function approximation architecture.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
