63 research outputs found
R&D offshoring and technology learning in emerging economies: Firm-level evidence from the ICT industry
This paper studies the impact of the R&D offshoring of multinational enterprises on the firms in host emerging economies. We develop a two-stage non-cooperative game to analyze the strategic interaction between multinational and host country enterprises engaged in R&D investment. An empirical analysis of 12,309 manufacturing firms in the ICT industry in China shows that R&D offshoring has a positive effect on the intensity of the R&D of host country firms. However, the magnitude of the impact depends on both the technological and geographical distance between the multinational and host country firms. The policy implications of these findings are that the governments of host country should be cautious about allowing advanced multinational R&D investment in under-developed sectors, but they should encourage such investment in developed sectors; and that local governments should be involved in R&D policy making because the positive impact of multinational R&D offshoring diminishes as the geographical distance between the multinational and host country firms increases.Research and Development, Offshoring, Spillovers, Emerging Economies
BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation
Cross-modal Unsupervised Domain Adaptation (UDA) aims to exploit the
complementarity of 2D-3D data to overcome the lack of annotation in a new
domain. However, UDA methods rely on access to the target domain during
training, meaning the trained model only works in a specific target domain. In
light of this, we propose cross-modal learning under bird's-eye view for Domain
Generalization (DG) of 3D semantic segmentation, called BEV-DG. DG is more
challenging because the model cannot access the target domain during training,
meaning it needs to rely on cross-modal learning to alleviate the domain gap.
Since 3D semantic segmentation requires the classification of each point,
existing cross-modal learning is directly conducted point-to-point, which is
sensitive to the misalignment in projections between pixels and points. To this
end, our approach aims to optimize domain-irrelevant representation modeling
with the aid of cross-modal learning under bird's-eye view. We propose
BEV-based Area-to-area Fusion (BAF) to conduct cross-modal learning under
bird's-eye view, which has a higher fault tolerance for point-level
misalignment. Furthermore, to model domain-irrelevant representations, we
propose BEV-driven Domain Contrastive Learning (BDCL) with the help of
cross-modal learning under bird's-eye view. We design three domain
generalization settings based on three 3D datasets, and BEV-DG significantly
outperforms state-of-the-art competitors with tremendous margins in all
settings.Comment: Accepted by ICCV 202
Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud
Existing methods for large-scale point cloud semantic segmentation require
expensive, tedious and error-prone manual point-wise annotations. Intuitively,
weakly supervised training is a direct solution to reduce the cost of labeling.
However, for weakly supervised large-scale point cloud semantic segmentation,
too few annotations will inevitably lead to ineffective learning of network. We
propose an effective weakly supervised method containing two components to
solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,}
point cloud colorization, with a self-supervised learning to transfer the
learned prior knowledge from a large amount of unlabeled point cloud to a
weakly supervised network. In this way, the representation capability of the
weakly supervised network can be improved by the guidance from a heterogeneous
task. Besides, to generate pseudo label for unlabeled data, a sparse label
propagation mechanism is proposed with the help of generated class prototypes,
which is used to measure the classification confidence of unlabeled point. Our
method is evaluated on large-scale point cloud datasets with different
scenarios including indoor and outdoor. The experimental results show the large
gain against existing weakly supervised and comparable results to fully
supervised methods\footnote{Code based on mindspore:
https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}
Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification
The Information Bottleneck (IB) provides an information theoretic principle
for representation learning, by retaining all information relevant for
predicting label while minimizing the redundancy. Though IB principle has been
applied to a wide range of applications, its optimization remains a challenging
problem which heavily relies on the accurate estimation of mutual information.
In this paper, we present a new strategy, Variational Self-Distillation (VSD),
which provides a scalable, flexible and analytic solution to essentially
fitting the mutual information but without explicitly estimating it. Under
rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic
correlation between representation and label for supervised training.
Furthermore, by extending VSD to multi-view learning, we introduce two other
strategies, Variational Cross-Distillation (VCD) and Variational
Mutual-Learning (VML), which significantly improve the robustness of
representation to view-changes by eliminating view-specific and task-irrelevant
information. To verify our theoretically grounded strategies, we apply our
approaches to cross-modal person Re-ID, and conduct extensive experiments,
where the superior performance against state-of-the-art methods are
demonstrated. Our intriguing findings highlight the need to rethink the way to
estimate mutua
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning
Weakly supervised point cloud semantic segmentation methods that require 1\%
or fewer labels, hoping to realize almost the same performance as fully
supervised approaches, which recently, have attracted extensive research
attention. A typical solution in this framework is to use self-training or
pseudo labeling to mine the supervision from the point cloud itself, but ignore
the critical information from images. In fact, cameras widely exist in LiDAR
scenarios and this complementary information seems to be greatly important for
3D applications. In this paper, we propose a novel cross-modality weakly
supervised method for 3D segmentation, incorporating complementary information
from unlabeled images. Basically, we design a dual-branch network equipped with
an active labeling strategy, to maximize the power of tiny parts of labels and
directly realize 2D-to-3D knowledge transfer. Afterwards, we establish a
cross-modal self-training framework in an Expectation-Maximum (EM) perspective,
which iterates between pseudo labels estimation and parameters updating. In the
M-Step, we propose a cross-modal association learning to mine complementary
supervision from images by reinforcing the cycle-consistency between 3D points
and 2D superpixels. In the E-step, a pseudo label self-rectification mechanism
is derived to filter noise labels thus providing more accurate labels for the
networks to get fully trained. The extensive experimental results demonstrate
that our method even outperforms the state-of-the-art fully supervised
competitors with less than 1\% actively selected annotations
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