The recent development of online static map element (a.k.a. HD Map)
cons...
We introduce a method to convert Physics-Informed Neural Networks (PINNs...
Few-shot image generation, which aims to produce plausible and diverse i...
High-definition (HD) map provides abundant and precise static environmen...
This paper presents a mini immersed finite element (IFE) method for solv...
We study a fundamental problem in optimization under uncertainty. There ...
We study universal rates for multiclass classification, establishing the...
Reproducibility of results is a cornerstone of the scientific method.
Sc...
Image restoration aims to reconstruct degraded images, e.g., denoising o...
Large-scale road surface reconstruction is becoming important to autonom...
Real-world programs expecting structured inputs often has a format-parsi...
In this paper, we develop an arbitrary-order locking-free enriched Galer...
High-definition (HD) map serves as the essential infrastructure of auton...
Open-world instance segmentation has recently gained significant
popular...
The archaeological dating of bronze dings has played a critical role in ...
Learning-based approaches have achieved impressive performance for auton...
Autonomous driving requires a comprehensive understanding of the surroun...
Online lane graph construction is a promising but challenging task in
au...
Background: To develop an artificial intelligence system that can accura...
Objective: Reliable tools to predict moyamoya disease (MMD) patients at ...
In this paper, we develop two fully nonconforming (both H(grad
curl)-non...
Spiking neural networks (SNNs) have been widely used due to their strong...
In this paper, we introduce a novel approach for ground plane normal
est...
Motion prediction is highly relevant to the perception of dynamic object...
Parallel tempering (PT), also known as replica exchange, is the go-to
wo...
We propose a Spiking Neural Network (SNN)-based explicit numerical schem...
In advanced paradigms of autonomous driving, learning Bird's Eye View (B...
General movement assessment (GMA) of infant movement videos (IMVs) is an...
Labeling objects with pixel-wise segmentation requires a huge amount of ...
Visual-Semantic Embedding (VSE) aims to learn an embedding space where
r...
Next basket recommender systems (NBRs) aim to recommend a user's next
(s...
In order to get raw images of high quality for downstream Image Signal
P...
We present MapTR, a structured end-to-end framework for efficient online...
Unlike the conventional Knowledge Distillation (KD), Self-KD allows a ne...
In this work, we propose PolarBEV for vision-based uneven BEV representa...
3D detection based on surround-view camera system is a critical techniqu...
We propose two families of nonconforming elements on cubical meshes: one...
The query mechanism introduced in the DETR method is changing the paradi...
Learning Bird's Eye View (BEV) representation from surrounding-view came...
The estimation of cumulative distribution functions (CDF) is an importan...
This paper introduces contrastive siamese (c-siam) network, an architect...
Privacy-preserving is a key problem for the machine learning algorithm.
...
One of the main broad applications of deep learning is function regressi...
The performance of nighttime semantic segmentation is restricted by the ...
There have been significant advancements in dynamic novel view synthesis...
Current Knowledge Distillation (KD) methods for semantic segmentation of...
Active camera relocalization (ACR) is a new problem in computer vision t...
Deep learning has shown impressive performance in semantic segmentation,...
This paper proposes an Allen-Cahn Chan-Vese model to settle the multi-ph...
Studying the inherent symmetry of data is of great importance in machine...