121 research outputs found
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion
RGB-guided depth completion aims at predicting dense depth maps from sparse
depth measurements and corresponding RGB images, where how to effectively and
efficiently exploit the multi-modal information is a key issue. Guided dynamic
filters, which generate spatially-variant depth-wise separable convolutional
filters from RGB features to guide depth features, have been proven to be
effective in this task. However, the dynamically generated filters require
massive model parameters, computational costs and memory footprints when the
number of feature channels is large. In this paper, we propose to decompose the
guided dynamic filters into a spatially-shared component multiplied by
content-adaptive adaptors at each spatial location. Based on the proposed idea,
we introduce two decomposition schemes A and B, which decompose the filters by
splitting the filter structure and using spatial-wise attention, respectively.
The decomposed filters not only maintain the favorable properties of guided
dynamic filters as being content-dependent and spatially-variant, but also
reduce model parameters and hardware costs, as the learned adaptors are
decoupled with the number of feature channels. Extensive experimental results
demonstrate that the methods using our schemes outperform state-of-the-art
methods on the KITTI dataset, and rank 1st and 2nd on the KITTI benchmark at
the time of submission. Meanwhile, they also achieve comparable performance on
the NYUv2 dataset. In addition, our proposed methods are general and could be
employed as plug-and-play feature fusion blocks in other multi-modal fusion
tasks such as RGB-D salient object detection
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
Existing deep learning-based depth completion methods generally employ
massive stacked layers to predict the dense depth map from sparse input data.
Although such approaches greatly advance this task, their accompanied huge
computational complexity hinders their practical applications. To accomplish
depth completion more efficiently, we propose a novel lightweight deep network
framework, the Long-short Range Recurrent Updating (LRRU) network. Without
learning complex feature representations, LRRU first roughly fills the sparse
input to obtain an initial dense depth map, and then iteratively updates it
through learned spatially-variant kernels. Our iterative update process is
content-adaptive and highly flexible, where the kernel weights are learned by
jointly considering the guidance RGB images and the depth map to be updated,
and large-to-small kernel scopes are dynamically adjusted to capture
long-to-short range dependencies. Our initial depth map has coarse but complete
scene depth information, which helps relieve the burden of directly regressing
the dense depth from sparse ones, while our proposed method can effectively
refine it to an accurate depth map with less learnable parameters and inference
time. Experimental results demonstrate that our proposed LRRU variants achieve
state-of-the-art performance across different parameter regimes. In particular,
the LRRU-Base model outperforms competing approaches on the NYUv2 dataset, and
ranks 1st on the KITTI depth completion benchmark at the time of submission.
Project page: https://npucvr.github.io/LRRU/.Comment: Published in ICCV 202
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular
Depth Estimation Challenge (MDEC). This edition was open to methods using any
form of supervision, including fully-supervised, self-supervised, multi-task or
proxy depth. The challenge was based around the SYNS-Patches dataset, which
features a wide diversity of environments with high-quality dense ground-truth.
This includes complex natural environments, e.g. forests or fields, which are
greatly underrepresented in current benchmarks.
The challenge received eight unique submissions that outperformed the
provided SotA baseline on any of the pointcloud- or image-based metrics. The
top supervised submission improved relative F-Score by 27.62%, while the top
self-supervised improved it by 16.61%. Supervised submissions generally
leveraged large collections of datasets to improve data diversity.
Self-supervised submissions instead updated the network architecture and
pretrained backbones. These results represent a significant progress in the
field, while highlighting avenues for future research, such as reducing
interpolation artifacts at depth boundaries, improving self-supervised indoor
performance and overall natural image accuracy.Comment: Published at CVPRW202
Influence of common genetic variation on lung cancer risk: meta-analysis of 14 900 cases and 29 485 controls
Recent genome-wide association studies (GWASs) have identified common genetic variants at 5p15.33, 6p21-6p22 and 15q25.1 associated with lung cancer risk. Several other genetic regions including variants of CHEK2 (22q12), TP53BP1 (15q15) and RAD52 (12p13) have been demonstrated to influence lung cancer risk in candidate- or pathway-based analyses. To identify novel risk variants for lung cancer, we performed a meta-analysis of 16 GWASs, totaling 14 900 cases and 29 485 controls of European descent. Our data provided increased support for previously identified risk loci at 5p15 (P = 7.2 × 10−16), 6p21 (P = 2.3 × 10−14) and 15q25 (P = 2.2 × 10−63). Furthermore, we demonstrated histology-specific effects for 5p15, 6p21 and 12p13 loci but not for the 15q25 region. Subgroup analysis also identified a novel disease locus for squamous cell carcinoma at 9p21 (CDKN2A/p16INK4A/p14ARF/CDKN2B/p15INK4B/ANRIL; rs1333040, P = 3.0 × 10−7) which was replicated in a series of 5415 Han Chinese (P = 0.03; combined analysis, P = 2.3 × 10−8). This large analysis provides additional evidence for the role of inherited genetic susceptibility to lung cancer and insight into biological differences in the development of the different histological types of lung cance
Chilling acclimation provides immunity to stress by altering regulatory networks and inducing genes with protective functions in Cassava
Reduced pulmonary function and increased pro-inflammatory cytokines in nanoscale carbon black-exposed workers
Feature Activation through First Power Linear Unit with Sign
The activation function represents a crucial component in the design of a convolutional neural network (CNN). It enables the efficient extraction of multiple features from visual patterns, and introduces systemic non-linearity to data processing. This paper proposes a novel and insightful activation method termed FPLUS, which exploits mathematical power function with polar signs in form. It is enlightened by common inverse operations while endowed with an intuitive meaning of bionics. The formulation is derived theoretically under conditions of some prior knowledge and anticipative properties. Subsequently, its feasibility is verified through a series of experiments using typical benchmark datasets. The results indicate that our approach bears superior competitiveness among numerous activation functions, as well as compatible stability across many CNN architectures. Furthermore, we extend the function presented to a more generalized type called PFPLUS with two parameters that can be fixed or learnable, so as to augment its expressive capacity. The outcomes of identical tests serve to validate this improvement. Therefore, we believe the work in this paper holds a certain value in enriching the family of activation units
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