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Found 16 papers, 4 papers with code
Neural Radiance Field in Autonomous Driving: A Survey
To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.
DepthMOT: Depth Cues Lead to a Strong Multi-Object Tracker
Inspired by this, even though the bounding boxes of objects are close on the camera plane, we can differentiate them in the depth dimension, thereby establishing a 3D perception of the objects.
DiffYOLO: Object Detection for Anti-Noise via YOLO and Diffusion Models
Object detection models represented by YOLO series have been widely used and have achieved great results on the high quality datasets, but not all the working conditions are ideal.
SANeRF-HQ: Segment Anything for NeRF in High Quality
Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view synthesis.
centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains.
Building a digital twin of EDFA: a grey-box modeling approach
To enable intelligent and self-driving optical networks, high-accuracy physical layer models are required.
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
We abstract the features (i. e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions.
Instance Neural Radiance Field
This paper presents one of the first learning-based NeRF 3D instance segmentation pipelines, dubbed as {\bf \inerflong}, or \inerf.
ONeRF: Unsupervised 3D Object Segmentation from Multiple Views
We present ONeRF, a method that automatically segments and reconstructs object instances in 3D from multi-view RGB images without any additional manual annotations.
NeRF-RPN: A general framework for object detection in NeRFs
This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF.
A Grey-box Launch-profile Aware Model for C+L Band Raman Amplification
Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression.
Physics-informed EDFA Gain Model Based on Active Learning
We propose a physics-informed EDFA gain model based on the active learning method.
Learning Visual Styles from Audio-Visual Associations
Our model learns to manipulate the texture of a scene to match a sound, a problem we term audio-driven image stylization.
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese.
Modality Laziness: Everybody's Business is Nobody's Business
We name this problem of multi-modal training, \emph{Modality Laziness}.
Improving Multi-Modal Learning with Uni-Modal Teachers
We name this problem Modality Failure, and hypothesize that the imbalance of modalities and the implicit bias of common objectives in fusion method prevent encoders of each modality from sufficient feature learning.
Ranked #60 on
Semantic Segmentation
on NYU Depth v2
