Qing Liu

EAAI Journal 2026 Journal Article

Maintaining the consistency of small targets on invariant deep semantic structures

• Haifeng Sang
• Yuwei Wu
• Qing Liu
• Chenxin Liu
• Xinyan Chang
• Dakuo He

In Infrared Small Target Detection (IRSTD), weak target signals and low contrast make the boundaries of small targets difficult to distinguish from complex backgrounds. The multi-level downsampling in the encoder further attenuates boundary information, while upsampling and cross-layer fusion in the decoder may amplify residual noise and pseudo-edge responses. The combination of these effects poses significant challenges for accurate boundary reconstruction and semantic discrimination. To address this issue, we propose the Edge-Target Deep Semantic Consistency (ET-DSC) semantic adaptive balancing framework: in encoder, shallow-layer modeling and gated fusion are adopted to enhance target boundaries; in decoder, semantic consistency constraints are introduced to preserve real boundaries and suppress false edges. Furthermore, a semantic allocation mechanism is established between shallow and deep layers to achieve cooperative optimization between edge compensation and semantic preservation. Experimental results on multiple public IRSTD datasets demonstrate that ET-DSC effectively reconstructs small-target boundaries and achieves higher localization accuracy under complex and low Signal-to-Noise Ratio(SNR) conditions. This work provides a reliable framework for fine-grained modeling of small targets in infrared scenes and offers new insights for future IRSTD network design. The codes are available at https: //github. com/Yuweiw-1024/ET-DSC.

AAAI Conference 2024 Conference Paper

Amodal Scene Analysis via Holistic Occlusion Relation Inference and Generative Mask Completion

• Bowen Zhang
• Qing Liu
• Jianming Zhang
• Yilin Wang
• Liyang Liu
• Zhe Lin
• Yifan Liu

Amodal scene analysis entails interpreting the occlusion relationship among scene elements and inferring the possible shapes of the invisible parts. Existing methods typically frame this task as an extended instance segmentation or a pair-wise object de-occlusion problem. In this work, we propose a new framework, which comprises a Holistic Occlusion Relation Inference (HORI) module followed by an instance-level Generative Mask Completion (GMC) module. Unlike previous approaches, which rely on mask completion results for occlusion reasoning, our HORI module directly predicts an occlusion relation matrix in a single pass. This approach is much more efficient than the pair-wise de-occlusion process and it naturally handles mutual occlusion, a common but often neglected situation. Moreover, we formulate the mask completion task as a generative process and use a diffusion-based GMC module for instance-level mask completion. This improves mask completion quality and provides multiple plausible solutions. We further introduce a large-scale amodal segmentation dataset with high-quality human annotations, including mutual occlusions. Experiments on our dataset and two public benchmarks demonstrate the advantages of our method. code public available at https://github.com/zbwxp/Amodal-AAAI.

JBHI Journal 2024 Journal Article

ER-GET: Emotion Recognition Based on Global ECG Trajectory

• Ya Li
• Runxi Tan
• Tianxin Lin
• Qing Liu
• Chang-Dong Wang
• Min Chen

In recent years, the recognition of human emotions based on electrocardiogram (ECG) signals has been considered a novel area of study among researchers. Despite the challenge of extracting latent emotion information from ECG signals, existing methods are able to recognize emotions by calculating the heart rate variability (HRV) features. However, such local features have drawbacks, as they do not provide a comprehensive description of ECG signals, leading to suboptimal recognition performance. For the first time, we propose a new strategy to extract hidden emotional information from the global ECG trajectory for emotion recognition. Specifically, a period of ECG signals is decomposed into sub-signals of different frequency bands through ensemble empirical mode decomposition (EEMD), and a series of multi-sequence trajectory graphs is constructed by orthogonally combining these sub-signals to extract latent emotional information. Additionally, to better utilize these graph features, a network has been designed that includes self-supervised graph representation learning and ensemble learning for classification. This approach surpasses recent notable works, achieving outstanding results, with an accuracy of 95. 08% in arousal and 95. 90% in valence detection. Additionally, this global feature is compared and discussed in relation to HRV features, with the intention of providing inspiration for subsequent research.

YNICL Journal 2024 Journal Article

Right superior frontal gyrus: A potential neuroimaging biomarker for predicting short-term efficacy in schizophrenia

• Yongfeng Yang
• Xueyan Jin
• Yongjiang Xue
• Xue Li
• Yi Chen
• Ning Kang
• Wei Yan
• Peng Li

Antipsychotic drug treatment for schizophrenia (SZ) can alter brain structure and function, but it is unclear if specific regional changes are associated with treatment outcome. Therefore, we examined the effects of antipsychotic drug treatment on regional grey matter (GM) density, white matter (WM) density, and functional connectivity (FC) as well as associations between regional changes and treatment efficacy. SZ patients (n = 163) and health controls (HCs) (n = 131) were examined by structural magnetic resonance imaging (sMRI) at baseline, and a subset of SZ patients (n = 77) were re-examined after 8 weeks of second-generation antipsychotic treatment to assess changes in regional GM and WM density. In addition, 88 SZ patients and 81 HCs were examined by resting-state functional MRI (rs-fMRI) at baseline and the patients were re-examined post-treatment to examine FC changes. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) were applied to measure psychiatric symptoms and cognitive impairments in SZ. SZ patients were then stratified into response and non-response groups according to PANSS score change (≥50 % decrease or <50 % decrease, respectively). The GM density of the right cingulate gyrus, WM density of the right superior frontal gyrus (SFG) plus 5 other WM tracts were reduced in the response group compared to the non-response group. The FC values between the right anterior cingulate and paracingulate gyrus and left thalamus were reduced in the entire SZ group (n = 88) after treatment, while FC between the right inferior temporal gyrus (ITG) and right medial superior frontal gyrus (SFGmed) was increased in the response group. There were no significant changes in regional FC among the non-response group after treatment and no correlations with symptom or cognition test scores. These findings suggest that the right SFG is a critical target of antipsychotic drugs and that WM density and FC alterations within this region could be used as potential indicators in predicting the treatment outcome of antipsychotics of SZ.

YNICL Journal 2023 Journal Article

Cortical anatomical variations, gene expression profiles, and clinical phenotypes in patients with schizophrenia

• Yong Han
• Yongfeng Yang
• Zhilu Zhou
• Xueyan Jin
• Han Shi
• Minglong Shao
• Meng Song
• Xi Su

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) patients display significant structural brain abnormalities; nevertheless, the genetic mechanisms regulating cortical anatomical variations and their correlation with the disease phenotype are still ambiguous. STUDY DESIGN: We characterized anatomical variation using a surface-based method derived from structural magnetic resonance imaging of patients with SZ and age- and sex-matched healthy controls (HCs). Partial least-squares regression was performed across cortex regions between anatomical variation and average transcriptional profiles of SZ risk genes and all qualified genes from the Allen Human Brain Atlas. The morphological features of each brain region were correlated to symptomology variables in patients with SZ using partial correlation analysis. STUDY RESULTS: A total of 203 SZ and 201 HCs were included in the final analysis. We observed significant variation of 55 regions of cortical thickness, 23 regions of volume, 7 regions of area, and 55 regions of local gyrification index (LGI) between SZ and HC groups. Expression profiles of 4 SZ risk genes and 96 genes from all qualified genes showed a correlation to anatomical variability, however, after multiple comparisons, the correlations were no longer significant. LGI variability in multiple frontal subregions was associated with specific symptoms of SZ, whereas cognitive function involving attention/vigilance was linked to LGI variability across nine brain regions. CONCLUSIONS: Cortical anatomical variation of patients with schizophrenia is associated with gene transcriptome profiles as well as clinical phenotypes.

JBHI Journal 2023 Journal Article

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

• Yixiong Liang
• Shuo Feng
• Qing Liu
• Hulin Kuang
• Jianfeng Liu
• Liyan Liao
• Yun Du
• Jianxin Wang

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposal. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with a feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we show that the proposed feature-enhancing scheme can facilitate image- and smear-level classification.

NeurIPS Conference 2023 Conference Paper

PHOTOSWAP: Personalized Subject Swapping in Images

• Jing Gu
• Yilin Wang
• Nanxuan Zhao
• Tsu-Jui Fu
• Wei Xiong
• Qing Liu
• Zhifei Zhang
• He Zhang

In an era where images and visual content dominate our digital landscape, the ability to manipulate and personalize these images has become a necessity. Envision seamlessly substituting a tabby cat lounging on a sunlit window sill in a photograph with your own playful puppy, all while preserving the original charm and composition of the image. We present \emph{Photoswap}, a novel approach that enables this immersive image editing experience through personalized subject swapping in existing images. \emph{Photoswap} first learns the visual concept of the subject from reference images and then swaps it into the target image using pre-trained diffusion models in a training-free manner. We establish that a well-conceptualized visual subject can be seamlessly transferred to any image with appropriate self-attention and cross-attention manipulation, maintaining the pose of the swapped subject and the overall coherence of the image. Comprehensive experiments underscore the efficacy and controllability of \emph{Photoswap} in personalized subject swapping. Furthermore, \emph{Photoswap} significantly outperforms baseline methods in human ratings across subject swapping, background preservation, and overall quality, revealing its vast application potential, from entertainment to professional editing.

AAAI Conference 2023 Conference Paper

TOT：Topology-Aware Optimal Transport for Multimodal Hate Detection

• Linhao Zhang
• Li Jin
• Xian Sun
• Guangluan Xu
• Zequn Zhang
• Xiaoyu Li
• Nayu Liu
• Qing Liu

Multimodal hate detection, which aims to identify the harmful content online such as memes, is crucial for building a wholesome internet environment. Previous work has made enlightening exploration in detecting explicit hate remarks. However, most of their approaches neglect the analysis of implicit harm, which is particularly challenging as explicit text markers and demographic visual cues are often twisted or missing. The leveraged cross-modal attention mechanisms also suffer from the distributional modality gap and lack logical interpretability. To address these semantic gap issues, we propose TOT: a topology-aware optimal transport framework to decipher the implicit harm in memes scenario, which formulates the cross-modal aligning problem as solutions for optimal transportation plans. Specifically, we leverage an optimal transport kernel method to capture complementary information from multiple modalities. The kernel embedding provides a non-linear transformation ability to reproduce a kernel Hilbert space (RKHS), which reflects significance for eliminating the distributional modality gap. Moreover, we perceive the topology information based on aligned representations to conduct bipartite graph path reasoning. The newly achieved state-of-the-art performance on two publicly available benchmark datasets, together with further visual analysis, demonstrate the superiority of TOT in capturing implicit cross-modal alignment.

YNICL Journal 2022 Journal Article

Abnormal patterns of regional homogeneity and functional connectivity across the adolescent first-episode, adult first-episode and adult chronic schizophrenia

• Yongfeng Yang
• Yuqing Sun
• Yuliang Zhang
• Xueyan Jin
• Zheng Li
• Minli Ding
• Han Shi
• Qing Liu

Functional deficits in schizophrenia (SZ) are observed prior to the onset of psychosis and differ at different stages of SZ. However, there is a paucity of studies focused on adolescent first-episode SZ (AOS), adult first-episode SZ (AFES), and adult chronic SZ (CHSZ). In this study, we investigated regional activity and corresponding functional connectivity alterations that have aimed to compare the three disease stages simultaneously. The subjects comprised 49 patients with AOS, 57 patients with AFES, 51 patients with CHSZ, 41 adolescent healthy controls, and 138 adult healthy controls. We compared regional homogeneity (ReHo) between patients at each disease stage with matched healthy controls. We focused on the shared brain regions that showed significant differences between SZ patients at the three different disease stages and healthy controls. Further analysis was conducted to explore whether the patterns of the whole brain functional connectivity alterations were similar. The putamen and medial frontal gyrus (MFG) showed consistently abnormal patterns in AOS, AFES, and CHSZ. Commonly decreased ReHo values in the MFG and increased ReHo values in the bilateral putamen were found in AOS, AFES, and CHSZ. Functional connectivity of MFG remained common abnormality in different SZ stage. In conclusion, ReHo abnormalities in the MFG and the putamen may be common abnormal patterns of brain function in the three different stages of SZ. The vmPFC-dlPFC FC abnormality common occurs in adolescence and adulthood.. This study may provide a more comprehensive understanding of the neurodevelopmental abnormality across the AOS, AFES, and CHSZ.

JBHI Journal 2022 Journal Article

Dual-Branch Network With Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation in Color Fundus Images

• Qing Liu
• Haotian Liu
• Yang Zhao
• Yixiong Liang

Automated segmentation of hard exudates in colour fundus images is a challenge task due to issues of extreme class imbalance and enormous size variation. This paper aims to tackle these issues and proposes a dual-branch network with dual-sampling modulated Dice loss. It consists of two branches: large hard exudate biased segmentation branch and small hard exudate biased segmentation branch. Both of them are responsible for their own duties separately. Furthermore, we propose a dual-sampling modulated Dice loss for the training such that our proposed dual-branch network is able to segment hard exudates in different sizes. In detail, for the first branch, we use a uniform sampler to sample pixels from predicted segmentation mask for Dice loss calculation, which leads to this branch naturally be biased in favour of large hard exudates as Dice loss generates larger cost on misidentification of large hard exudates than small hard exudates. For the second branch, we use a re-balanced sampler to oversample hard exudate pixels and undersample background pixels for loss calculation. In this way, cost on misidentification of small hard exudates is enlarged, which enforces the parameters in the second branch fit small hard exudates well. Considering that large hard exudates are much easier to be correctly identified than small hard exudates, we propose an easy-to-difficult learning strategy by adaptively modulating the losses of two branches. We evaluate our proposed method on two public datasets and the results demonstrate that ours achieves state-of-the-art performance.

AAAI Conference 2022 Conference Paper

PolygonE: Modeling N-ary Relational Data as Gyro-Polygons in Hyperbolic Space

• Shiyao Yan
• Zequn Zhang
• Xian Sun
• Guangluan Xu
• Shuchao Li
• Qing Liu
• Nayu Liu
• Shensi Wang

N-ary relational knowledge base (KBs) embedding aims to map binary and beyond-binary facts into low-dimensional vector space simultaneously. Existing approaches typically decompose n-ary relational facts into subtuples, and they generally model n-ary relational KBs in Euclidean space. However, n-ary relational facts are semantically and structurally intact; decomposition undermines the semantical and structural integrity. Moreover, compared to the binary relational KBs, n-ary ones are characterized by more abundant and complicated hierarchy structures, which could not be well expressed in Euclidean space. To address the issues, we propose a gyro-polygon embedding framework to realize n-ary fact integrity keeping and hierarchy capturing, termed PolygonE. Specifically, n-ary relational facts are modeled as gyropolygons in the hyperbolic space, where we denote entities in facts as vertexes of gyro-polygons and relations as entity translocation operations. Importantly, we design a fact plausibility measuring strategy based on the vertex-gyrocentroid geodesic to optimize the relation-adjusted gyro-polygon. Experimental results demonstrate that PolygonE shows SOTA performance on all benchmark datasets and generalizes well on binary data. Finally, we also visualize the embedding to help comprehend PolygonE’s awareness of hierarchies.

IS Journal 2021 Journal Article

Answer Keyword Generation for Community Question Answering by Multiaspect Gamma–Poisson Matrix Completion

• Qing Liu
• Trong Dinh Thac Do
• Longbing Cao

Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data are often sparse and decentralized, and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This article addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multiaspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma–Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w. r. t. recommending appropriate and informative answer keywords.

FLAP Journal 2021 Journal Article

Large-scale Legal Reasoning with Rules and Databases.

• Grigoris Antoniou
• George Baryannis
• Sotiris Batsakis
• Guido Governatori
• Mohammad Badiul Islam
• Qing Liu
• Livio Robaldo
• Giovanni Siragusa

EAAI Journal 2019 Journal Article

Derive knowledge of Z-number from the perspective of Dempster–Shafer evidence theory

• Qing Liu
• Ye Tian
• Bingyi Kang

Z-number, combined with constraint and reliability of the information, is an effective frame to simulate the thinking of humans. How to derive knowledge of Z-numbers, especially from the objective data may become a fascinating and open issue. In this paper, a method of deriving knowledge of Z-numbers from the perspective of Dempster–Shafer theory is proposed. The proposed method considers the Z-number generating from objective and subjective data using Dempster–Shafer theory. Some numerical examples and experimental simulations are used to illustrate the effectiveness of the proposed methodology.

AAMAS Conference 2019 Conference Paper

Dynamic Source Weight Computation for Truth Inference over Data Streams

• Yi Yang
• Quan Bai
• Qing Liu

Truth inference, a method that resolves conflicts among multi-agent data, has been widely studied in the field of AI. Most existing truth inference methods use iterative approaches to achieve high accuracy, but are inefficient to infer object truths over data streams. The methods developed for streaming data can achieve high efficiency but suffer from low accuracy. In this paper, we propose a novel truth inference method, Dynamic Source Weight Computation truth inference (DSWC), that can work with a wide range of iterative-based truth inference methods to dynamically compute source weights over data streams. Specifically, we use Taylor expansion to analyze the unit error of object truths inferred by source weights computed at a previous timestamp. If the source weight at present is predicted to be able to limit the error under a threshold, we use the source weights computed previously to approximate object truths at present to avoid the expensive source weight computation step. Compared with the existing work, the proposed method is more effective in predicting source weights and can be applied to a wider range of applications. Experimental results based on four real-world datasets demonstrate that DSWC is both accurate and efficient for truth inference over data streams.

AAMAS Conference 2019 Conference Paper

Modeling Random Guessing and Task Difficulty for Truth Inference in Crowdsourcing

• Yi Yang
• Quan Bai
• Qing Liu

This paper addresses the challenge of truth inference in crowdsourcing applications. We propose a generative method that jointly models tasks’ difficulties, workers’ abilities and guessing behavior to estimate the truths of crowdsourced tasks, which leads to a more accurate estimation on the workers’ abilities and tasks’ truths. Experiments demonstrate that the proposed method is more effective for estimating truths of crowdsourced tasks compared with the state-of-art methods.