arXiv subjects

Xinyue Li

Publications and source records attributed to Xinyue Li.

60 records · Page 4Linked to original sources

arXiv2023

Camouflaged Object Detection with Feature Grafting and Distractor Aware

The task of Camouflaged Object Detection (COD) aims to accurately segment camouflaged objects that integrated into the environment, which is more challenging than ordinary detection as the texture between the target and background is visually indistinguishable. In this paper, we proposed a novel Feature Grafting and Distractor Aware network (FDNet) to handle the COD task. Specifically, we use CNN and Transformer to encode multi-scale images in parallel. In order to better explore the advantages of the two encoders, we design a cross-attention-based Feature Grafting Module to graft features extracted from Transformer branch into CNN branch, after which the features are aggregated in the Feature Fusion Module. A Distractor Aware Module is designed to explicitly model the two possible distractors in the COD task to refine the coarse camouflage map. We also proposed the largest artificial camouflaged object dataset which contains 2000 images with annotations, named ACOD2K. We conducted extensive experiments on four widely used benchmark datasets and the ACOD2K dataset. The results show that our method significantly outperforms other state-of-the-art methods. The code and the ACOD2K will be available at https://github.com/syxvision/FDNet.

cs.CV↗

arXiv2023

Identifying the most predictive risk factors for future cognitive impairment among elderly Chinese

Introduction. The societal burden of cognitive impairments in China has prompted researchers to develop clinical prediction models aimed at making risk assessments that enable preventative interventions. However, it is unclear which risk factors best predict future cognitive impairment and if predictive ability is consistent across different socioeconomic groups. Methods. We quantified the ability of demographics, instrumental activities of daily living, activities of daily living, cognitive tests, social factors, psychological factors, diet, exercise and sleep, chronic diseases, and three recently published prediction models predict future cognitive impairments in the general Chinese population and among male, female, rural, urban, educated, and uneducated elderly. Data were taken from the 2011 and 2014 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Results. The risk factor groups with the most predictive ability in the general population were demographics (AUC, 0.78, 95% CI, 0.77-0.78), cognitive tests (AUC, 0.72, 95% CI, 0.72-0.73), and instrumental activities of daily living (AUC, 0.71, 95% CI, 0.70-0.71). Demographics, cognitive tests, instrumental activities of daily living, and all three re-created prediction models had significantly higher AUCs when making predictions among women compared to men and among the uneducated compared to the educated. Discussion. This study suggests that demographics, cognitive tests, and instrumental activities of daily living are the most useful risk factors for predicting future cognitive impairment among elderly Chinese. However, the most useful risk factors and existing models have lower predictive power among male, urban, and educated elderly. More efforts are needed to ensure that equally accurate risk assessments can be conducted across different socioeconomic groups in China.

stat.AP↗

arXiv2023

Robust Calibrate Proxy Loss for Deep Metric Learning

The mainstream researche in deep metric learning can be divided into two genres: proxy-based and pair-based methods. Proxy-based methods have attracted extensive attention due to the lower training complexity and fast network convergence. However, these methods have limitations as the poxy optimization is done by network, which makes it challenging for the proxy to accurately represent the feature distrubtion of the real class of data. In this paper, we propose a Calibrate Proxy (CP) structure, which uses the real sample information to improve the similarity calculation in proxy-based loss and introduces a calibration loss to constraint the proxy optimization towards the center of the class features. At the same time, we set a small number of proxies for each class to alleviate the impact of intra-class differences on retrieval performance. The effectiveness of our method is evaluated by extensive experiments on three public datasets and multiple synthetic label-noise datasets. The results show that our approach can effectively improve the performance of commonly used proxy-based losses on both regular and noisy datasets.

cs.IR↗

arXiv2023

Measurement of $^{19}$F($p$,$γ$)$^{20}$Ne reaction suggests CNO break-out in first stars

The origin of calcium production in the first stars (Pop III stars), which formed out of the primordial matter of the Big Bang, and their fates, remain most fascinating mysteries in astrophysics. Advanced nuclear burning and supernovae were thought to be the dominant source of the Ca production seen in all stars. Here we report on a qualitatively different path to Ca production through break-out from the "warm" carbon-nitrogen-oxygen (CNO) cycle. We extend direct measurement of the $^{19}$F($p$, $γ$)$^{20}$Ne break-out reaction down to an unprecedentedly low energy point of 186 keV and discover a key resonance at 225 keV. In the domain of astrophysical interest, at around 0.1 giga kelvin, this thermonuclear $^{19}$F($p$,$γ$)$^{20}$Ne rate is up to a factor of 7.4 larger than the previous recommended rate. Our stellar models show a stronger break-out during stellar hydrogen burning than thought before, and may reveal the nature of Ca production in Pop III stars imprinted on the oldest known ultra-iron poor star, SMSS0313-6708. This result from the China Jinping Underground Laboratory, the deepest laboratory in the world, offering an environment with extremely low cosmic-ray induced background, has far-reaching implications on our understanding of how the first stars evolve and die. Our rate showcases the impact that faint Pop III star supernovae can have on the nucleosynthesis observed in the oldest known stars and first galaxies, key mission targets of the James Webb Space Telescope.

astro-ph.SR↗

arXiv2023

PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

With the increasing attention to large vision-language models such as CLIP, there has been a significant amount of effort dedicated to building efficient prompts. Unlike conventional methods of only learning one single prompt, we propose to learn multiple comprehensive prompts to describe diverse characteristics of categories such as intrinsic attributes or extrinsic contexts. However, directly matching each prompt to the same visual feature is problematic, as it pushes the prompts to converge to one point. To solve this problem, we propose to apply optimal transport to match the vision and text modalities. Specifically, we first model images and the categories with visual and textual feature sets. Then, we apply a two-stage optimization strategy to learn the prompts. In the inner loop, we optimize the optimal transport distance to align visual features and prompts by the Sinkhorn algorithm, while in the outer loop, we learn the prompts by this distance from the supervised data. Extensive experiments are conducted on the few-shot recognition task and the improvement demonstrates the superiority of our method. The code is available at https://github.com/CHENGY12/PLOT.

cs.CV↗

arXiv2020

A Hidden Markov Model Based Unsupervised Algorithm for Sleep/Wake Identification Using Actigraphy

Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification. In this study, we proposed a Hidden Markov Model (HMM) based unsupervised algorithm that can automatically and effectively infer sleep/wake states. It is an individualized data-driven approach that analyzes actigraphy from each individual respectively to learn activity characteristics and further separate sleep and wake states. We used Actiwatch and polysomnography (PSG) data from 43 individuals in the Multi-Ethnic Study of Atherosclerosis to evaluate the performance of our method. Epoch-by-epoch comparisons were made between our HMM algorithm and that embedded in the Actiwatch software (AS). The percent agreement between HMM and PSG was 85.7%, and that between AS and PSG was 84.7%. Positive predictive values for sleep epochs were 85.6% and 84.6% for HMM and AS, respectively, and 95.5% and 85.6% for wake epochs. Both methods have similar performance and tend to overestimate sleep and underestimate wake compared to PSG. Our HMM approach is able to quantify the variability in activity counts that allow us to differentiate relatively active and sedentary individuals: individuals with higher estimated variabilities tend to show more frequent sedentary behaviors. In conclusion, our unsupervised data-driven HMM algorithm achieves slightly better performance compared to the commonly used algorithm in the Actiwatch software. HMM can help expand the application of actigraphy in large-scale studies and in cases where intrusive PSG is hard to acquire or unavailable. In addition, the estimated HMM parameters can characterize individual activity patterns that can be utilized for further analysis.

stat.AP↗