325 research outputs found
Weakly-supervised Pre-training for 3D Human Pose Estimation via Perspective Knowledge
Modern deep learning-based 3D pose estimation approaches require plenty of 3D
pose annotations. However, existing 3D datasets lack diversity, which limits
the performance of current methods and their generalization ability. Although
existing methods utilize 2D pose annotations to help 3D pose estimation, they
mainly focus on extracting 2D structural constraints from 2D poses, ignoring
the 3D information hidden in the images. In this paper, we propose a novel
method to extract weak 3D information directly from 2D images without 3D pose
supervision. Firstly, we utilize 2D pose annotations and perspective prior
knowledge to generate the relationship of that keypoint is closer or farther
from the camera, called relative depth. We collect a 2D pose dataset (MCPC) and
generate relative depth labels. Based on MCPC, we propose a weakly-supervised
pre-training (WSP) strategy to distinguish the depth relationship between two
points in an image. WSP enables the learning of the relative depth of two
keypoints on lots of in-the-wild images, which is more capable of predicting
depth and generalization ability for 3D human pose estimation. After
fine-tuning on 3D pose datasets, WSP achieves state-of-the-art results on two
widely-used benchmarks
DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks
Modeling dynamics in the form of partial differential equations (PDEs) is an
effectual way to understand real-world physics processes. For complex physics
systems, analytical solutions are not available and numerical solutions are
widely-used. However, traditional numerical algorithms are computationally
expensive and challenging in handling multiphysics systems. Recently, using
neural networks to solve PDEs has made significant progress, called
physics-informed neural networks (PINNs). PINNs encode physical laws into
neural networks and learn the continuous solutions of PDEs. For the training of
PINNs, existing methods suffer from the problems of inefficiency and unstable
convergence, since the PDE residuals require calculating automatic
differentiation. In this paper, we propose Dynamic Mesh-based Importance
Sampling (DMIS) to tackle these problems. DMIS is a novel sampling scheme based
on importance sampling, which constructs a dynamic triangular mesh to estimate
sample weights efficiently. DMIS has broad applicability and can be easily
integrated into existing methods. The evaluation of DMIS on three widely-used
benchmarks shows that DMIS improves the convergence speed and accuracy in the
meantime. Especially in solving the highly nonlinear Schr\"odinger Equation,
compared with state-of-the-art methods, DMIS shows up to 46% smaller root mean
square error and five times faster convergence speed. Code are available at
https://github.com/MatrixBrain/DMIS.Comment: Accepted to AAAl-2
MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field
3D style transfer aims to render stylized novel views of 3D scenes with the
specified style, which requires high-quality rendering and keeping multi-view
consistency. Benefiting from the ability of 3D representation from Neural
Radiance Field (NeRF), existing methods learn the stylized NeRF by giving a
reference style from an image. However, they suffer the challenges of
high-quality stylization with texture details for multi-style transfer and
stylization with multimodal guidance. In this paper, we reveal that the same
objects in 3D scenes show various states (color tone, details, etc.) from
different views after stylization since previous methods optimized by
single-view image-based style loss functions, leading NeRF to tend to smooth
texture details, further resulting in low-quality rendering. To tackle these
problems, we propose a novel Multimodal-guided 3D Multi-style transfer of NeRF,
termed MM-NeRF, which achieves high-quality 3D multi-style rendering with
texture details and can be driven by multimodal-style guidance. First, MM-NeRF
adopts a unified framework to project multimodal guidance into CLIP space and
extracts multimodal style features to guide the multi-style stylization. To
relieve the problem of lacking details, we propose a novel Multi-Head Learning
Scheme (MLS), in which each style head predicts the parameters of the color
head of NeRF. MLS decomposes the learning difficulty caused by the
inconsistency of multi-style transfer and improves the quality of stylization.
In addition, the MLS can generalize pre-trained MM-NeRF to any new styles by
adding heads with small training costs (a few minutes). Extensive experiments
on three real-world 3D scene datasets show that MM-NeRF achieves high-quality
3D multi-style stylization with multimodal guidance, keeps multi-view
consistency, and keeps semantic consistency of multimodal style guidance. Codes
will be released later
Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach
Graph Neural Networks (GNNs) are popular machine learning methods for
modeling graph data. A lot of GNNs perform well on homophily graphs while
having unsatisfactory performance on heterophily graphs. Recently, some
researchers turn their attention to designing GNNs for heterophily graphs by
adjusting the message passing mechanism or enlarging the receptive field of the
message passing. Different from existing works that mitigate the issues of
heterophily from model design perspective, we propose to study heterophily
graphs from an orthogonal perspective by rewiring the graph structure to reduce
heterophily and making the traditional GNNs perform better. Through
comprehensive empirical studies and analysis, we verify the potential of the
rewiring methods. To fully exploit its potential, we propose a method named
Deep Heterophily Graph Rewiring (DHGR) to rewire graphs by adding homophilic
edges and pruning heterophilic edges. The detailed way of rewiring is
determined by comparing the similarity of label/feature-distribution of node
neighbors. Besides, we design a scalable implementation for DHGR to guarantee
high efficiency. DHRG can be easily used as a plug-in module, i.e., a graph
pre-processing step, for any GNNs, including both GNN for homophily and
heterophily, to boost their performance on the node classification task. To the
best of our knowledge, it is the first work studying graph rewiring for
heterophily graphs. Extensive experiments on 11 public graph datasets
demonstrate the superiority of our proposed methods.Comment: 11 page
Robust Mid-Pass Filtering Graph Convolutional Networks
Graph convolutional networks (GCNs) are currently the most promising paradigm
for dealing with graph-structure data, while recent studies have also shown
that GCNs are vulnerable to adversarial attacks. Thus developing GCN models
that are robust to such attacks become a hot research topic. However, the
structural purification learning-based or robustness constraints-based defense
GCN methods are usually designed for specific data or attacks, and introduce
additional objective that is not for classification. Extra training overhead is
also required in their design. To address these challenges, we conduct in-depth
explorations on mid-frequency signals on graphs and propose a simple yet
effective Mid-pass filter GCN (Mid-GCN). Theoretical analyses guarantee the
robustness of signals through the mid-pass filter, and we also shed light on
the properties of different frequency signals under adversarial attacks.
Extensive experiments on six benchmark graph data further verify the
effectiveness of our designed Mid-GCN in node classification accuracy compared
to state-of-the-art GCNs under various adversarial attack strategies.Comment: Accepted by WWW'2
Insecticidal Activity of the Soil in the Rhizosphere of Viburnum odoratissimum against Solenopsis invicta (Hymenoptera: Formicidae)
Methyl salicylate produced by Viburnum odoratissimum is known to exert lethal or sublethal effects on insects. Replacing conventional pesticides with insecticidal plants is necessary for environmental protection. We evaluated the behavioral and toxicological responses of the red imported fire ant (RIFA Solenopsis invicta)( Hymenopter: Buren Formicidae) at different soil depths in the rhizosphere of V. odoratissimum. Results of insecticidal activity bioassays indicated that the mortality for minor and major ants in soil at depths of 0-10 cm at days 11 and 12 both ranged from 68.75% to 100.00%, with repellent rates of 83.54%–100.00% and 85.31%–100.00%, respectively. In behavioral ability tests, 85.45%–100.00% of minor ants and 86.74%–94.85% of major ants lost their ability to grasp after nine days, with crawl rates at only 0.00%–29.25% and 0.00%–55.77%, respectively. Therefore, we conclude from the result that the soil under V. odoratissimum at depths of 0-10 cm exhibited excellent insecticidal effect in controlling RIFA.Methyl salicylate produced by Viburnum odoratissimum is known to exert lethal or sublethal effects on insects. Replacing conventional pesticides with insecticidal plants is necessary for environmental protection. We evaluated the behavioral and toxicological responses of the red imported fire ant (RIFA Solenopsis invicta, Buren) (Hymenoptera: Formicidae) at different soil depths in the rhizosphere of V. odoratissimum. Results of insecticidal activity bioassays indicated that the mortality for minor and major ants in soil at depths of 0-10 cm at days 11 and 12 both ranged from 68.75% to 100.00%, with repellency rates of 83.54%-100.00% and 85.31%-100.00%, respectively. In behavioral ability tests, 85.45%-100.00% of minor ants and 86.74%-94.85% of major ants lost their ability to grasp after nine days, with crawling rates at only 0.00%-29.25% and 0.00%-55.77%, respectively. Therefore, we conclude from the result that the soil under V. odoratissimum at depths of 0-10 cm exhibited excellent insecticidal effect in controlling RIFA
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