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Jun Guo

Publications and source records attributed to Jun Guo.

At least 73 records · Page 4Linked to original sources
arXiv2024

A Bernoulli-barycentric rational matrix collocation method with preconditioning for a class of evolutionary PDEs

We propose a Bernoulli-barycentric rational matrix collocation method for two-dimensional evolutionary partial differential equations (PDEs) with variable coefficients that combines Bernoulli polynomials with barycentric rational interpolations in time and space, respectively. The theoretical accuracy $O\left((2π)^{-N}+h_x^{d_x-1}+h_y^{d_y-1}\right)$ of our numerical scheme is proven, where $N$ is the number of basis functions in time, $h_x$ and $h_y$ are the grid sizes in the $x$, $y$-directions, respectively, and $0\leq d_x\leq \frac{b-a}{h_x},~0\leq d_y\leq\frac{d-c}{h_y}$. For the efficient solution of the relevant linear system arising from the discretizations, we introduce a class of dimension expanded preconditioners that take the advantage of structural properties of the coefficient matrices, and we present a theoretical analysis of eigenvalue distributions of the preconditioned matrices. The effectiveness of our proposed method and preconditioners are studied for solving some real-world examples represented by the heat conduction equation, the advection-diffusion equation, the wave equation and telegraph equations.

math.NA↗
arXiv2024

Massive Gauge Theory with Quasigluon for Hot $SU(N)$: Phase Transition and Thermodynamics

It is challenging to build a model that can correctly and unifiedly account for the deconfinement phase transition and thermodynamics of the hot $SU(N)$ pure Yang-Mills (PYM) system, for any $N$. In this article, we slightly generalize the massive PYM model to the situation with a quasigluon mass $M_g(T)$ varying with temperature, inspired by the quasigluon model. In such a framework, we can acquire an effective potential for the temporal gauge field background by perturbative calculation, rather than adding by hand. The resulting potential works well to describe the behavior of the hot PYM system for all $N$, via the single parameter $M_g(T)$. Moreover, under the assumption of unified eigenvalue distribution, the $M_g(T)$ fitted by machine learning is found to follow $N$-universality.

hep-ph↗
arXiv2024

Unsupervised Long-Term Person Re-Identification with Clothes Change

We investigate unsupervised person re-identification (Re-ID) with clothes change, a new challenging problem with more practical usability and scalability to real-world deployment. Most existing re-id methods artificially assume the clothes of every single person to be stationary across space and time. This condition is mostly valid for short-term re-id scenarios since an average person would often change the clothes even within a single day. To alleviate this assumption, several recent works have introduced the clothes change facet to re-id, with a focus on supervised learning person identity discriminative representation with invariance to clothes changes. Taking a step further towards this long-term re-id direction, we further eliminate the requirement of person identity labels, as they are significantly more expensive and more tedious to annotate in comparison to short-term person re-id datasets. Compared to conventional unsupervised short-term re-id, this new problem is drastically more challenging as different people may have similar clothes whilst the same person can wear multiple suites of clothes over different locations and times with very distinct appearance. To overcome such obstacles, we introduce a novel Curriculum Person Clustering (CPC) method that can adaptively regulate the unsupervised clustering criterion according to the clustering confidence. Experiments on three long-term person re-id datasets show that our CPC outperforms SOTA unsupervised re-id methods and even closely matches the supervised re-id models.

cs.CV↗
arXiv2023

Vision-language Assisted Attribute Learning

Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization. Existing attribute learning methods often treat the missing labels as negative or simply ignore them all during training, either of which could hamper the model performance to a great extent. To overcome these limitations, in this paper we leverage the available vision-language knowledge to explicitly disclose the missing labels for enhancing model learning. Given an image, we predict the likelihood of each missing attribute label assisted by an off-the-shelf vision-language model, and randomly select to ignore those with high scores in training. Our strategy strikes a good balance between fully ignoring and negatifying the missing labels, as these high scores are found to be informative on revealing label ambiguity. Extensive experiments show that our proposed vision-language assisted loss can achieve state-of-the-art performance on the newly cleaned VAW dataset. Qualitative evaluation demonstrates the ability of the proposed method in predicting more complete attributes.

cs.CV↗
arXiv2023

Laser frequency stabilization and photoacoustic detection based on the tapered fiber coupled crystalline resonator

We demonstrate laser frequency stabilization using a high-Q MgF2 crystalline whispering gallery mode resonator coupled with a tapered fiber. We discovered that the tapered fiber, acting as a microcantilever, exhibits mechanical resonance characteristics that is capable of transmitting acoustic perturbations to the frequency locking loop. Both experimental and theoretical investigations into the influence of external acoustic waves on the coupling system were conducted. After acoustic isolation, the locked laser exhibits a minimum frequency noise of 0.4Hz2/Hz at 7kHz and an integral linewidth of 68Hz (0.1s integration time). Benefiting from the ultralow frequency noise of the stabilized laser, it achieves a minimum noise equivalent acoustic signal level of 4.76*10-4 Pa/Hz1/2. Our results not only facilitate the realization of ultralow noise lasers but also serves as a novel and sensitive photoacoustic detector.

physics.optics↗
arXiv2023

HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues and Physical Priors

Recent methods for dynamic human reconstruction have attained promising reconstruction results. Most of these methods rely only on RGB color supervision without considering explicit geometric constraints. This leads to existing human reconstruction techniques being more prone to overfitting to color and causes geometrically inherent ambiguities, especially in the sparse multi-view setup. Motivated by recent advances in the field of monocular geometry prediction, we consider the geometric constraints of estimated depth and normals in the learning of neural implicit representation for dynamic human reconstruction. As a geometric regularization, this provides reliable yet explicit supervision information, and improves reconstruction quality. We also exploit several beneficial physical priors, such as adding noise into view direction and maximizing the density on the human surface. These priors ensure the color rendered along rays to be robust to view direction and reduce the inherent ambiguities of density estimated along rays. Experimental results demonstrate that depth and normal cues, predicted by human-specific monocular estimators, can provide effective supervision signals and render more accurate images. Finally, we also show that the proposed physical priors significantly reduce overfitting and improve the overall quality of novel view synthesis. Our code is available at:~\href{https://github.com/PRIS-CV/HumanRecon}{https://github.com/PRIS-CV/HumanRecon}.

cs.CV↗
arXiv2023

Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks

Despite the broad application of Machine Learning models as a Service (MLaaS), they are vulnerable to model stealing attacks. These attacks can replicate the model functionality by using the black-box query process without any prior knowledge of the target victim model. Existing stealing defenses add deceptive perturbations to the victim's posterior probabilities to mislead the attackers. However, these defenses are now suffering problems of high inference computational overheads and unfavorable trade-offs between benign accuracy and stealing robustness, which challenges the feasibility of deployed models in practice. To address the problems, this paper proposes Isolation and Induction (InI), a novel and effective training framework for model stealing defenses. Instead of deploying auxiliary defense modules that introduce redundant inference time, InI directly trains a defensive model by isolating the adversary's training gradient from the expected gradient, which can effectively reduce the inference computational cost. In contrast to adding perturbations over model predictions that harm the benign accuracy, we train models to produce uninformative outputs against stealing queries, which can induce the adversary to extract little useful knowledge from victim models with minimal impact on the benign performance. Extensive experiments on several visual classification datasets (e.g., MNIST and CIFAR10) demonstrate the superior robustness (up to 48% reduction on stealing accuracy) and speed (up to 25.4x faster) of our InI over other state-of-the-art methods. Our codes can be found in https://github.com/DIG-Beihang/InI-Model-Stealing-Defense.

cs.CR↗
arXiv2023

Enhancing Cell Proliferation and Migration by MIR-Carbonyl Vibrational Coupling: Insights from Transcriptome Profiling

Cell proliferation and migration highly relate to normal tissue self-healing, therefore it is highly significant for artificial controlling. Recently, vibrational strong coupling between biomolecules and Mid-infrared (MIR) light photons has been successfully used to modify in vitro bioreactions, neuronal signaling and even animal behavior. However, the synergistic effects from molecules to cells remains unclear, and the regulation of MIR on cells needs to be explained from the molecular level. Herein, the proliferation rate and migration capacity of fibroblasts were increased by 156% and 162.5%, respectively, by vibratory coupling of 5.6 micrometers photons with carbonyl groups in biomolecules. Through transcriptome sequencing analysis, the regulatory mechanism of infrared light in 5.6 micrometers was explained from the level of signal pathway and cell components. 5.6 micrometers optical high power lasers can regulate cell function through vibrational strong coupling while minimizing photothermal damage. This work not only sheds light on the non-thermal effect on MIR light-based on wound healing, but also provides new evidence to future frequency medicine.

q-bio.GN↗
arXiv2023

Regulating the Hydrophobic Domain in Peptide-Catecholamine Coassembled Nanostructures for Fluorescence Enhancement

Hydrophobic domains provide specific microenvironment for essential functional activities in life. Herein, we studied how the coassembling of peptides with catecholamines regulate the hydrophobic domain-containing nanostructures for fluorescence enhancement. By peptide encoding and coassembling with catecholamines of different hydrophilicities, a series of hierarchical assembling systems were constructed. In combination with molecular dynamics simulation, we experimentally discovered the hydrophobic domain of chromophore microenvironment regulates the fluorescence of coassembled nanostructures. Our results shed light on the rational design of fluorescent bio-coassembled nanoprobes for biomedical applications.

q-bio.BM↗
arXiv2023

X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

Adversarial attacks are valuable for evaluating the robustness of deep learning models. Existing attacks are primarily conducted on the visible light spectrum (e.g., pixel-wise texture perturbation). However, attacks targeting texture-free X-ray images remain underexplored, despite the widespread application of X-ray imaging in safety-critical scenarios such as the X-ray detection of prohibited items. In this paper, we take the first step toward the study of adversarial attacks targeted at X-ray prohibited item detection, and reveal the serious threats posed by such attacks in this safety-critical scenario. Specifically, we posit that successful physical adversarial attacks in this scenario should be specially designed to circumvent the challenges posed by color/texture fading and complex overlapping. To this end, we propose X-adv to generate physically printable metals that act as an adversarial agent capable of deceiving X-ray detectors when placed in luggage. To resolve the issues associated with color/texture fading, we develop a differentiable converter that facilitates the generation of 3D-printable objects with adversarial shapes, using the gradients of a surrogate model rather than directly generating adversarial textures. To place the printed 3D adversarial objects in luggage with complex overlapped instances, we design a policy-based reinforcement learning strategy to find locations eliciting strong attack performance in worst-case scenarios whereby the prohibited items are heavily occluded by other items. To verify the effectiveness of the proposed X-Adv, we conduct extensive experiments in both the digital and the physical world (employing a commercial X-ray security inspection system for the latter case). Furthermore, we present the physical-world X-ray adversarial attack dataset XAD.

cs.CR↗
arXiv2023

MeV Gamma-Ray Constraints for Light Dark Matter from Semi-Annihilation

Exploring the realm of Dark Matter research, Light DM, which has a mass in the range of 1 MeV to 1 GeV, is a fascinating topic both theoretically and experimentally. We assume that the light dark matter is composed of complex scalars and produced from semi-annihilation, which is close to the scale of the MeV Gamma-ray satellite, allowing us to explore the implications of this hypothesis. The experimental data we used to constrain the scenario is from five different sources: the COMPTEL, EGRET, INTEGRAL, Fermi Gamma-ray Space Telescope, and the e-ASTROGAM future reach. We use the analytical formula to measure the X-ray spectra allowing us to determine the annihilation cross-section bounds from $10^{-28}\mathrm{cm}^3/\mathrm{s}$ to $10^{-22}\mathrm{cm}^3/\mathrm{s}$ for different combinations of dark matter and mediator masses. We found that the MeV gamma-ray provides valuable insight into the structure of the semi-annihilation dark matter, where EGRET contributes to the stringent constraint to the semi-annihilation, and the e-ASTROGAM future reach could probe the whole parameter space of the model.

hep-ph↗
arXiv2023

Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification

The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i.e., lower inter-class variations and higher intra-class variations). To alleviate this problem, prior works predominately use a support set to reconstruct the query image and then utilize metric learning to determine its category. Upon careful inspection, we further reveal that such unidirectional reconstruction methods only help to increase inter-class variations and are not effective in tackling intra-class variations. In this paper, we for the first time introduce a bi-reconstruction mechanism that can simultaneously accommodate for inter-class and intra-class variations. In addition to using the support set to reconstruct the query set for increasing inter-class variations, we further use the query set to reconstruct the support set for reducing intra-class variations. This design effectively helps the model to explore more subtle and discriminative features which is key for the fine-grained problem in hand. Furthermore, we also construct a self-reconstruction module to work alongside the bi-directional module to make the features even more discriminative. Experimental results on three widely used fine-grained image classification datasets consistently show considerable improvements compared with other methods. Codes are available at: https://github.com/PRIS-CV/Bi-FRN.

cs.CV↗
arXiv2022

A Comprehensive Evaluation Framework for Deep Model Robustness

Deep neural networks (DNNs) have achieved remarkable performance across a wide range of applications, while they are vulnerable to adversarial examples, which motivates the evaluation and benchmark of model robustness. However, current evaluations usually use simple metrics to study the performance of defenses, which are far from understanding the limitation and weaknesses of these defense methods. Thus, most proposed defenses are quickly shown to be attacked successfully, which results in the ``arm race'' phenomenon between attack and defense. To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i.e., data and model). Through neuron coverage and data imperceptibility, we use data-oriented metrics to measure the integrity of test examples; by delving into model structure and behavior, we exploit model-oriented metrics to further evaluate robustness in the adversarial setting. To fully demonstrate the effectiveness of our framework, we conduct large-scale experiments on multiple datasets including CIFAR-10, SVHN, and ImageNet using different models and defenses with our open-source platform. Overall, our paper provides a comprehensive evaluation framework, where researchers could conduct comprehensive and fast evaluations using the open-source toolkit, and the analytical results could inspire deeper understanding and further improvement to the model robustness.

cs.CV↗
arXiv2022

Learning Invariant Visual Representations for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and a composition into a common embedding space to measure their compatibility score. However, both attributes and objects share the visual representations learned above, leading the model to exploit spurious correlations and bias towards seen pairs. Instead, we reconsider CZSL as an out-of-distribution generalization problem. If an object is treated as a domain, we can learn object-invariant features to recognize the attributes attached to any object reliably. Similarly, attribute-invariant features can also be learned when recognizing the objects with attributes as domains. Specifically, we propose an invariant feature learning framework to align different domains at the representation and gradient levels to capture the intrinsic characteristics associated with the tasks. Experiments on two CZSL benchmarks demonstrate that the proposed method significantly outperforms the previous state-of-the-art.

cs.CV↗
arXiv2022

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

cs.CV↗
arXiv2022

Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the quality of clustering depends heavily on the quality of the learned features, which are overwhelmingly dominated by the colors in images especially in the unsupervised setting. In this paper, we propose a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which cluster structure is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework. To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively. Extensive experiments conducted on three benchmark datasets demonstrate superior performance of our proposal.

cs.CV↗
arXiv2022

Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning

While deep neural networks (DNNs) have strengthened the performance of cooperative multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by adversarial examples. Considering the safety critical applications of c-MARL, such as traffic management, power management and unmanned aerial vehicle control, it is crucial to test the robustness of c-MARL algorithm before it was deployed in reality. Existing adversarial attacks for MARL could be used for testing, but is limited to one robustness aspects (e.g., reward, state, action), while c-MARL model could be attacked from any aspect. To overcome the challenge, we propose MARLSafe, the first robustness testing framework for c-MARL algorithms. First, motivated by Markov Decision Process (MDP), MARLSafe consider the robustness of c-MARL algorithms comprehensively from three aspects, namely state robustness, action robustness and reward robustness. Any c-MARL algorithm must simultaneously satisfy these robustness aspects to be considered secure. Second, due to the scarceness of c-MARL attack, we propose c-MARL attacks as robustness testing algorithms from multiple aspects. Experiments on \textit{SMAC} environment reveals that many state-of-the-art c-MARL algorithms are of low robustness in all aspect, pointing out the urgent need to test and enhance robustness of c-MARL algorithms.

cs.MA↗
arXiv2022

Duplex Contextual Relation Network for Polyp Segmentation

Polyp segmentation is of great importance in the early diagnosis and treatment of colorectal cancer. Since polyps vary in their shape, size, color, and texture, accurate polyp segmentation is very challenging. One promising way to mitigate the diversity of polyps is to model the contextual relation for each pixel such as using attention mechanism. However, previous methods only focus on learning the dependencies between the position within an individual image and ignore the contextual relation across different images. In this paper, we propose Duplex Contextual Relation Network (DCRNet) to capture both within-image and cross-image contextual relations. Specifically, we first design Interior Contextual-Relation Module to estimate the similarity between each position and all the positions within the same image. Then Exterior Contextual-Relation Module is incorporated to estimate the similarity between each position and the positions across different images. Based on the above two types of similarity, the feature at one position can be further enhanced by the contextual region embedding within and across images. To store the characteristic region embedding from all the images, a memory bank is designed and operates as a queue. Therefore, the proposed method can relate similar features even though they come from different images. We evaluate the proposed method on the EndoScene, Kvasir-SEG and the recently released large-scale PICCOLO dataset. Experimental results show that the proposed DCRNet outperforms the state-of-the-art methods in terms of the widely-used evaluation metrics.

eess.IV↗