DiffusionPhase: Motion Diffusion in Frequency Domain

In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e.g., ``A person walks forward"). Existing techniques struggle with motion diversity and smooth transitions in generating arbitrary-length motion sequences, due to limited text-to-motion datasets and the pose representations used that often lack expressiveness or compactness. To address these issues, we propose the first method for text-conditioned human motion generation in the frequency domain of motions. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space with high-frequency details encoded, capturing the local periodicity of motions in time and space with high accuracy. We also introduce a conditional diffusion model for predicting periodic motion parameters based on text descriptions and a start pose, efficiently achieving smooth transitions between motion sequences associated with different text descriptions. Experiments demonstrate that our approach outperforms current methods in generating a broader variety of high-quality motions, and synthesizing long sequences with natural transitions

TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer

In this paper, we introduce a set of effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images. Current SOTA performance is achieved by Transformer-based structures. However, they suffer from high model complexity and computation cost caused by redundant tokens. We propose token reduction strategies based on two important aspects, i.e., the 3D geometry structure and 2D image feature, where we hierarchically recover the mesh geometry with priors from body structure and conduct token clustering to pass fewer but more discriminative image feature tokens to the Transformer. As a result, our method vastly reduces the number of tokens involved in high-complexity interactions in the Transformer, achieving competitive accuracy of shape recovery at a significantly reduced computational cost. We conduct extensive experiments across a wide range of benchmarks to validate the proposed method and further demonstrate the generalizability of our method on hand mesh recovery. Our code will be publicly available once the paper is published

Resuscitation of Preterm Infants with Reduced Oxygen Results in Less Oxidative Stress than Resuscitation with 100% Oxygen

The objective of this study was to determine the effects of the level of inhaled oxygen during resuscitation on the levels of free radicals and anti-oxidative capacity in the heparinized venous blood of preterm infants. Forty four preterm infants <35 weeks of gestation with mild to moderate neonatal asphyxia were randomized into two groups. The first group of infants were resuscitated with 100% oxygen (100% O2 group), while in the other group (reduced O2 group), the oxygen concentration was titrated according to pulse oximeter readings. We measured total hydroperoxide (TH) and redox potential (RP) in the plasma within 60 min of birth. The integrated excessive oxygen (∑(FiO2-0.21) × Time(min)) was higher in the 100% O2 group than in the reduced O2 group (p<0.0001). TH was higher in the 100% O2 group than in the reduced O2 group (p<0.0001). RP was not different between the 100% O2 and reduced O2 groups (p = 0.399). RP/TH ratio was lower in the 100% O2 group than in the reduced O2 group (p<0.01). We conclude that in the resuscitation of preterm infants with mild to moderate asphyxia, oxidative stress can be reduced by lowering the inspired oxygen concentration using a pulse oximeter

MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks

Deep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect prediction. Existing studies have proposed various methods to detect the new adversarial attacks. However, new attack methods keep evolving constantly and yield new adversarial examples to bypass the existing detectors. It needs to collect tens of thousands samples to train detectors, while the new attacks evolve much more frequently than the high-cost data collection. Thus, this situation leads the newly evolved attack samples to remain in small scales. To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples. Specifically, the learning consists of a double-network framework: a task-dedicated network and a master network which alternatively learn the detection capability for either seen attack or a new attack. To validate the effectiveness of our approach, we construct the benchmarks with few-shot-fashion protocols based on three conventional datasets, i.e. CIFAR-10, MNIST and Fashion-MNIST. Comprehensive experiments are conducted on them to verify the superiority of our approach with respect to the traditional adversarial attack detection methods.Comment: 10 pages, 2 figures, accepted as the conference paper of Proceedings of the 27th ACM International Conference on Multimedia (MM'19