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Kun Wang

Publications and source records attributed to Kun Wang.

At least 361 records · Page 20Linked to original sources
arXiv2023

Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception.

cs.CV↗
arXiv2023

Few-shot Forgery Detection via Guided Adversarial Interpolation

The increase in face manipulation models has led to a critical issue in society - the synthesis of realistic visual media. With the emergence of new forgery approaches at an unprecedented rate, existing forgery detection methods suffer from significant performance drops when applied to unseen novel forgery approaches. In this work, we address the few-shot forgery detection problem by 1) designing a comprehensive benchmark based on coverage analysis among various forgery approaches, and 2) proposing Guided Adversarial Interpolation (GAI). Our key insight is that there exist transferable distribution characteristics between majority and minority forgery classes1. Specifically, we enhance the discriminative ability against novel forgery approaches via adversarially interpolating the forgery artifacts of the minority samples to the majority samples under the guidance of a teacher network. Unlike the standard re-balancing method which usually results in over-fitting to minority classes, our method simultaneously takes account of the diversity of majority information as well as the significance of minority information. Extensive experiments demonstrate that our GAI achieves state-of-the-art performances on the established few-shot forgery detection benchmark. Notably, our method is also validated to be robust to choices of majority and minority forgery approaches. The formal publication version is available in Pattern Recognition.

cs.CV↗
arXiv2023

The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field

Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoothing as they go deeper as models of computer vision realm. In this work, we conduct a systematic study of deeper GNN research trajectories. Our findings indicate that the current success of deep GNNs primarily stems from (I) the adoption of innovations from CNNs, such as residual/skip connections, or (II) the tailor-made aggregation algorithms like DropEdge. However, these algorithms often lack intrinsic interpretability and indiscriminately treat all nodes within a given layer in a similar manner, thereby failing to capture the nuanced differences among various nodes. To this end, we introduce the Snowflake Hypothesis -- a novel paradigm underpinning the concept of ``one node, one receptive field''. The hypothesis draws inspiration from the unique and individualistic patterns of each snowflake, proposing a corresponding uniqueness in the receptive fields of nodes in the GNNs. We employ the simplest gradient and node-level cosine distance as guiding principles to regulate the aggregation depth for each node, and conduct comprehensive experiments including: (1) different training schemes; (2) various shallow and deep GNN backbones, and (3) various numbers of layers (8, 16, 32, 64) on multiple benchmarks (six graphs including dense graphs with millions of nodes); (4) compare with different aggregation strategies. The observational results demonstrate that our hypothesis can serve as a universal operator for a range of tasks, and it displays tremendous potential on deep GNNs. It can be applied to various GNN frameworks, enhancing its effectiveness when operating in-depth, and guiding the selection of the optimal network depth in an explainable and generalizable way.

cs.LG↗
arXiv2023

Interface-tuning of ferroelectricity and quadruple-well state in CuInP$_2$S$_6$ via ferroelectric oxide

Ferroelectric van der Waals CuInP$_2$S$_6$ possesses intriguing quadruple-well states and negative piezoelectricity. Its technological implementation has been impeded by the relatively low Curie temperature (bulk $T_C$ ~42 °C) and the lack of precise domain control. Here we show that CuInP$_2$S$_6$ can be immune to the finite size effect and exhibits enhanced ferroelectricity, piezoelectricity, and polar alignment in the ultrathin limit when interfaced with ferroelectric oxide PbZr$_{0.2}$Ti$_{0.8}$O$_3$ films. Piezoresponse force microscopy studies reveal that the polar domains in thin CuInP$_2$S$_6$ fully conform to those of underlying PbZr$_{0.2}$Ti$_{0.8}$O$_3$, where the piezoelectric coefficient changes sign and increases sharply with reducing thickness. High temperature $in$ $situ$ domain imaging points to a significantly enhanced $T_C$ exceeding 200 °C for 13 nm CuInP$_2$S$_6$ on PbZr$_{0.2}$Ti$_{0.8}$O$_3$. Density functional theory modeling and Monte Carlo simulations show that the enhanced polar alignment and $T_C$ can be attributed to interface-mediated structure distortion in CuInP$_2$S$_6$. Our study provides an effective material strategy to engineer the polar properties of CuInP$_2$S$_6$ for flexible nanoelectronic, optoelectronic, and mechanical applications.

cond-mat.mtrl-sci↗
arXiv2023

Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease

Objective: Bleeding from gastroesophageal varices (GEV) is a medical emergency associated with high mortality. We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of the liver and spleen to precisely assess the risk of GEV and high-risk gastroesophageal varices (HRV). Design: A prospective multicenter study was conducted in patients with compensated advanced chronic liver disease. 305 patients were enrolled from 12 hospitals, and finally 265 patients were included, with 1136 liver stiffness measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images generated by 2D-SWE. We leveraged deep learning methods to uncover associations between image features and patient risk, and thus conducted models to predict GEV and HRV. Results: A multi-modality Deep Learning Risk Prediction model (DLRP) was constructed to assess GEV and HRV, based on LSM and SSM images, and clinical information. Validation analysis revealed that the AUCs of DLRP were 0.91 for GEV (95% CI 0.90 to 0.93, p < 0.05) and 0.88 for HRV (95% CI 0.86 to 0.89, p < 0.01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM. Moreover, DLPR was better than the model using individual parameters, including LSM and SSM images. In HRV prediction, the 2D-SWE images of SSM outperform LSM (p < 0.01). Conclusion: DLRP shows excellent performance in predicting GEV and HRV over canonical risk indicators LSM and SSM. Additionally, the 2D-SWE images of SSM provided more information for better accuracy in predicting HRV than the LSM.

q-bio.TO↗
arXiv2023

Safety Guaranteed Control for Spacecraft Inspection Mission

This paper investigates the safety guaranteed problem in spacecraft inspection missions, considering multiple position obstacles and logical attitude forbidden zones. In order to address this issue, we propose a control strategy based on control barrier functions, summarized as "safety check on kinematics" and "velocity tracking on dynamics" approach. The proposed approach employs control barrier functions to describe the obstacles and to generate safe velocities via the solution of a quadratic programming problem. Subsequently, we design a proportional-like controller based on the generated velocity, which, despite its simplicity, can ensure safety even in the presence of velocity tracking errors. The stability and safety of the system are rigorously analyzed in this paper. Furthermore, to account for model uncertainties and external disturbances, we incorporate an immersion and invariance-based disturbance observer in our design. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed control strategy.

eess.SY↗
arXiv2023

FX UMa: A New Heartbeat Binary System with Linear and Non-linear Tidal Oscillations and delta Sct Pulsations

We present a detailed analysis of an eclipsing double-lined binary FX UMa based on TESS photometry and newly acquired spectroscopic observations. The radial velocities and atmospheric parameters for each component star are obtained from the SONG high-resolution spectra. Combined with the radial-velocity measurements, our light-curve modeling yields absolute masses and radii of the two components. The Fourier amplitude spectrum of the residual light curve reveals a total of 103 frequencies with signal-to-noise ratio (S/N) > 4, including 12 independent frequencies, 17 multiples of the orbital frequency (Nforb), and 74 combination frequencies. Ten Nforb peaks with S/N > 10 have very high amplitudes and are likely due to tidally excited oscillations (TEOs). The remaining Nforb peaks (4 < S/N < 10) may be originated from the imperfect removal, or they are actually real TEOs. Four anharmonic frequencies can pair up and sum to give exact harmonics of the orbital frequency, suggesting the existence of non-linear tidal processes in the eccentric binary system FX UMa. Eight independent frequencies in the range of 20 to 32 day$^{-1}$ are typical low-order pressure modes of delta Scuti pulsators.

astro-ph.SR↗
arXiv2023

Adaptive Compatible Performance Control for Spacecraft Attitude Control under Motion Constraints with Guaranteed Accuracy

This paper focuses on the problem of spacecraft attitude control in the presence of time-varying parameter uncertainties and multiple constraints, accounting for angular velocity limitation, performance requirements, and input saturation. To tackle this problem, we propose a modified framework called Compatible Performance Control (CPC), which integrates the Prescribed Performance Control (PPC) scheme with a contradiction detection and alleviation strategy. Firstly, by introducing the Zeroing Barrier Function (ZBF) concept, we propose a detection strategy to yield judgment on the compatibility between the angular velocity constraint and the performance envelope constraint. Subsequently, we propose a projection operator-governed dynamical system with a varying upper bound to generate an appropriate bounded performance envelope-modification signal if a contradiction exists, thereby alleviating the contradiction and promoting compatibility within the system. Next, a dynamical filter technique is introduced to construct a bounded reference velocity signal to address the angular velocity limitation. Furthermore, we employ a time-varying gain technique to address the challenge posed by time-varying parameter uncertainties, further developing an adaptive strategy that exhibits robustness on disturbance rejection. By utilizing the proposed CPC scheme and time-varying gain adaptive strategy, we construct an adaptive CPC controller, which guarantees the ultimate boundedness of the system, and all constraints are satisfied simultaneously during the whole control process. Finally, numerical simulation results are presented to show the effectiveness of the proposed framework.

eess.SY↗
arXiv2023

Composite Triggered Intermittent Control for Constrained Spacecraft Attitude Tracking

This paper focuses on the spacecraft attitude control problem with intermittent actuator activation, taking into account the attitude rotation rate limitation and input saturation issue simultaneously. To address this problem, we first propose a composite event-trigger mechanism, which composed of two state-dependent trigger that governing the activation and deactivation of actuators. Subsequently, by introducing the cascaded decomposition of Backstepping control philosophy, the designed trigger mechanism is then applied to the decomposed dynamical subsystem, providing a layered intermittent stabilization strategy. Further, the basic intermittent attitude controller is extended to a "constrained version" by introducing a strictly bounded virtual control law and an input saturation compensation auxiliary system. By analyzing the local boundedness of the system on each inter-event time interval, a uniformly, strictly decreasing upper boundary of the lumped system is further characterized, thereby completing the proof of the system's uniformly ultimately boundedness (UUB). Finally, numerical simulation results are illustrated to demonstrate the effectiveness of the proposed scheme.

eess.SY↗
arXiv2023

ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models

Large pre-trained vision-language models have shown great prominence in transferring pre-acquired knowledge to various domains and downstream tasks with appropriate prompting or tuning. Existing prevalent tuning methods can be generally categorized into three genres: 1) prompt engineering by creating suitable prompt texts, which is time-consuming and requires domain expertise; 2) or simply fine-tuning the whole model, which is extremely inefficient; 3) prompt tuning through parameterized prompt embeddings with the text encoder. Nevertheless, all methods rely on the text encoder for bridging the modality gap between vision and language. In this work, we question the necessity of the cumbersome text encoder for a more lightweight and efficient tuning paradigm as well as more representative prompt embeddings closer to the image representations. To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives. By dropping the text encoder, we are able to significantly speed up the learning process, \eg, from about an hour to just ten minutes in our experiments for personalized text-to-image generation without impairing the generation quality. Moreover, our proposed approach is orthogonal to current existing tuning methods since the searched concept embeddings can be further utilized in the next stage of fine-tuning the pre-trained large models for boosting performance. Extensive experiments show that our approach can beat the prompt tuning and textual inversion methods in a variety of downstream tasks including objection detection, instance segmentation, and image generation. Our approach also shows better generalization capability for unseen concepts in specialized domains, such as the medical domain.

cs.CV↗
arXiv2023

A Superluminous Supernova Lightened by Collisions with Pulsational Pair-instability Shells

Superluminous supernovae are among the most energetic stellar explosions in the Universe, but their energy sources remain an open question. Here we present long-term observations of one of the closest examples of the hydrogen-poor subclass (SLSNe-I), SN~2017egm, revealing the most complicated known luminosity evolution of SLSNe-I. Three distinct post-peak bumps were recorded in its light curve collected at about $100$--350\,days after maximum brightness, challenging current popular power models such as magnetar, fallback accretion, and interaction between ejecta and a circumstellar shell. However, the complex light curve can be well modelled by successive interactions with multiple circumstellar shells with a total mass of about $6.8$--7.7\,M$_\odot$. In this scenario, large energy deposition from interaction-induced reverse shocks results in ionization of neutral oxygen in the supernova ejecta and hence a much lower nebular-phase line ratio of [O\,\textsc{i}] $\lambda6300$/([Ca\,\textsc{ii}] + [O\,\textsc{ii}]) $\lambda7300$ ($\sim 0.2$) compared with that derived for other superluminous and normal stripped-envelope SNe. The pre-existing multiple shells indicate that the progenitor of SN~2017egm experienced pulsational mass ejections triggered by pair instability within 2 years before explosion, in robust agreement with theoretical predictions for a pre-pulsation helium-core mass of 48--51\,M$_{\odot}$. Finally, this work shows that the final explosion product may be a black hole with about 40\,M$_{\odot}$, and has significant implication for the formation of such heavy black holes that have been recently observed by LIGO-Virgo gravitational wave detectors.

astro-ph.HE↗
arXiv2023

Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars

The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image Classification network), is designed to classify sources from WISE images to achieve a more accurate classification goal. IICnet shows good ability on the feature extraction of the WISE sources. Experiments demonstrates that the classification results of IICnet are superior to some other methods; it has obtained 96.2% accuracy for galaxies, 97.9% accuracy for quasars, and 96.4% accuracy for stars, and the Area Under Curve (AUC) of the IICnet classifier can reach more than 99%. In addition, the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16, GoogleNet, ResNet34, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster inference. The above proves that IICnet is an effective method to classify infrared sources.

astro-ph.IM↗
arXiv2023

Survey for Distant Stellar Aggregates in Galactic Disk: Detecting Two Thousand Star Clusters and Candidates, along with the Dwarf Galaxy IC10

Despite having data for over 10^9 stars from Gaia, only less than 10^4 star clusters and candidates have been discovered. Particularly, distant star clusters are rarely identified, due to the challenges posed by heavy extinction and great distance. However, Gaia data has continued to improve, enabling even fainter cluster members to be distinguished from field stars. In this work, we will introduce a star cluster search method based on the DBSCAN algorithm; we have made improvements to make it better suited for identifying clusters on dimmer and more distant stars. After removing member stars of known Gaia-based clusters, we have identified 2086 objects with |b|<10 deg, of which 1488 are highly reliable open star clusters, along with 569 candidates, 28 globular cluster candidates and 1 irregular galaxy IC 10 at low Galactic latitudes. We found that the proper motion of IC10 is similar yet slightly different from the water maser observations, which is an important result for the comparison with Gaia and VLBA. Besides, when compared with the star clusters appearing in Gaia DR2/EDR3, we have found nearly three times as many new objects above a distance of 5 kpc, including hundreds of them above Av > 5 mag. And it has enabled us to detect a higher number of old clusters, over a billion years old, that are difficult to detect due to observational limitations. Our findings significantly expand the remote cluster sample and enhance our understanding of the limits of Gaia DR3 data in stellar aggregates research. The full figure set for 2085 clusters can be seen in \url{https://nadc.china-vo.org/res/r101258/}

astro-ph.GA↗
arXiv2023

An Effective Crop-Paste Pipeline for Few-shot Object Detection

Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. However, detecting novel categories with only a few samples usually leads to the problem of misclassification. In FSOD, we notice the false positive (FP) of novel categories is prominent, in which the base categories are often recognized as novel ones. To address this issue, a novel data augmentation pipeline that Crops the Novel instances and Pastes them on the selected Base images, called CNPB, is proposed. There are two key questions to be answered: (1) How to select useful base images? and (2) How to combine novel and base data? We design a multi-step selection strategy to find useful base data. Specifically, we first discover the base images which contain the FP of novel categories and select a certain amount of samples from them for the base and novel categories balance. Then the bad cases, such as the base images that have unlabeled ground truth or easily confused base instances, are removed by using CLIP. Finally, the same category strategy is adopted, in which a novel instance with category n is pasted on the base image with the FP of n. During combination, a novel instance is cropped and randomly down-sized, and thus pasted at the assigned optimal location from the randomly generated candidates in a selected base image. Our method is simple yet effective and can be easy to plug into existing FSOD methods, demonstrating significant potential for use. Extensive experiments on PASCAL VOC and MS COCO validate the effectiveness of our method.

cs.CV↗
arXiv2023

Explore the Power of Synthetic Data on Few-shot Object Detection

Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. The few training samples restrict the performance of FSOD model. Recent text-to-image generation models have shown promising results in generating high-quality images. How applicable these synthetic images are for FSOD tasks remains under-explored. This work extensively studies how synthetic images generated from state-of-the-art text-to-image generators benefit FSOD tasks. We focus on two perspectives: (1) How to use synthetic data for FSOD? (2) How to find representative samples from the large-scale synthetic dataset? We design a copy-paste-based pipeline for using synthetic data. Specifically, saliency object detection is applied to the original generated image, and the minimum enclosing box is used for cropping the main object based on the saliency map. After that, the cropped object is randomly pasted on the image, which comes from the base dataset. We also study the influence of the input text of text-to-image generator and the number of synthetic images used. To construct a representative synthetic training dataset, we maximize the diversity of the selected images via a sample-based and cluster-based method. However, the severe problem of high false positives (FP) ratio of novel categories in FSOD can not be solved by using synthetic data. We propose integrating CLIP, a zero-shot recognition model, into the FSOD pipeline, which can filter 90% of FP by defining a threshold for the similarity score between the detected object and the text of the predicted category. Extensive experiments on PASCAL VOC and MS COCO validate the effectiveness of our method, in which performance gain is up to 21.9% compared to the few-shot baseline.

cs.CV↗
arXiv2023

Siamese DETR

Recent self-supervised methods are mainly designed for representation learning with the base model, e.g., ResNets or ViTs. They cannot be easily transferred to DETR, with task-specific Transformer modules. In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architecture in DETR. We consider learning view-invariant and detection-oriented representations simultaneously through two complementary tasks, i.e., localization and discrimination, in a novel multi-view learning framework. Two self-supervised pretext tasks are designed: (i) Multi-View Region Detection aims at learning to localize regions-of-interest between augmented views of the input, and (ii) Multi-View Semantic Discrimination attempts to improve object-level discrimination for each region. The proposed Siamese DETR achieves state-of-the-art transfer performance on COCO and PASCAL VOC detection using different DETR variants in all setups. Code is available at https://github.com/Zx55/SiameseDETR.

cs.CV↗
arXiv2023

Cross-Platform Comparison of Arbitrary Quantum Processes

In this work, we present a protocol for comparing the performance of arbitrary quantum processes executed on spatially or temporally disparate quantum platforms using Local Operations and Classical Communication (LOCC). The protocol involves sampling local unitary operators, which are then communicated to each platform via classical communication to construct quantum state preparation and measurement circuits. Subsequently, the local unitary operators are implemented on each platform, resulting in the generation of probability distributions of measurement outcomes. The max process fidelity is estimated from the probability distributions, which ultimately quantifies the relative performance of the quantum processes. Furthermore, we demonstrate that this protocol can be adapted for quantum process tomography. We apply the protocol to compare the performance of five quantum devices from IBM and the "Qianshi" quantum computer from Baidu via the cloud. Remarkably, the experimental results reveal that the protocol can accurately compare the performance of the quantum processes implemented on different quantum computers, requiring significantly fewer measurements than those needed for full quantum process tomography. We view our work as a catalyst for collaborative efforts in cross-platform comparison of quantum computers.

quant-ph↗
arXiv2023

FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning

Federated learning (FL) has recently become a hot research topic, in which Byzantine robustness, communication efficiency and privacy preservation are three important aspects. However, the tension among these three aspects makes it hard to simultaneously take all of them into account. In view of this challenge, we theoretically analyze the conditions that a communication compression method should satisfy to be compatible with existing Byzantine-robust methods and privacy-preserving methods. Motivated by the analysis results, we propose a novel communication compression method called consensus sparsification (ConSpar). To the best of our knowledge, ConSpar is the first communication compression method that is designed to be compatible with both Byzantine-robust methods and privacy-preserving methods. Based on ConSpar, we further propose a novel FL framework called FedREP, which is Byzantine-robust, communication-efficient and privacy-preserving. We theoretically prove the Byzantine robustness and the convergence of FedREP. Empirical results show that FedREP can significantly outperform communication-efficient privacy-preserving baselines. Furthermore, compared with Byzantine-robust communication-efficient baselines, FedREP can achieve comparable accuracy with the extra advantage of privacy preservation.

cs.LG↗