Xin Tian

EAAI Journal 2026 Journal Article

Microscopic image segmentation of harmful algal blooms using pyramid fusion enhancement and dual-branch network

• Gengkun Wu
• Chao Cui
• Yining Fan
• Yubing Li
• Xin Tian

Monitoring harmful algal blooms (HABs) is important for marine ecosystem protection. However, HABs microscopic images often suffer from wavelength-selective color attenuation, low contrast, and scattering blur, which severely impair segmentation. To address these degradations, we propose a task-oriented enhancement-and-segmentation framework for HABs microscopy. In the enhancement stage, cyclic color channel compensation and particle swarm color balancing restore inter-channel consistency, while adaptive pyramid fusion selectively recovers fragile algal structures without amplifying background impurities. For segmentation, we propose the Transformer Convolution Fusion Network (TCoF), a dual-branch architecture combining Transformer-based global context and CNN-based boundary details. Its core novelty lies in the proposed Multi-scale Feature Complementarity Module (MFCM), which uses CNN-derived boundary priors to explicitly guide Transformer feature aggregation across scales, thereby reducing attention drift in turbid backgrounds. In addition, the detail-aware spatial pyramid pooling module (DASPP) compensates for the contour-smoothing bias of standard ASPP and improves the delineation of thin algal structures. Experiments on the AICO Lab dataset show that the proposed framework achieves state-of-the-art performance with an mIoU of 91. 52%.

AAAI Conference 2026 Conference Paper

Video Mirror Detection with the Motion-in-Depth Cue

• Alex Warren
• Ke Xu
• Xin Tian
• Gary K. L. Tam
• Benjamin W. Wah
• Rynson W. H. Lau

Detecting mirror regions in RGB videos is essential for scene understanding in applications such as scene reconstruction and robotic navigation. Existing video mirror detectors typically rely on cues like inside-outside mirror correspondences and 2D motion inconsistencies. However, these methods often yield noisy or incomplete predictions when confronted with complex real-world video scenes, especially in areas with occlusion or limited visual features and motions. We observe that human perceive and navigate 3D occluded environments with remarkable ease, owing to Motion-in-Depth (MiD) perception. MiD integrates information from visual appearance (image colors and textures), the way objects move around us in 3D space (3D motions), and their relative distance from us (depth) to determine if something is approaching or receding and to support navigation. Motivated by this neuroscience mechanism, we introduce MiD-VMD, the first approach to explicitly model MiD for video mirror detection. MiD-VMD jointly utilizes contrastive 3D motion, depth, and image features through two novel modules based on a combinational QKV transformer architecture. The Motion-in-Depth Attention Learning (MiD-AL) module captures complementary relationships across these modalities with combinatorial attention and enforces a compact encoding to represent global 3D transformations, resulting in more accurate mirror detection and reduced motion artifacts. The Motion-in-Depth Boundary Detection (MiD-BD) module further sharpens mirror boundaries by leveraging cross-modal attention on 3D motion and depth features. Extensive experiments show that MiD-VMD outperforms current SOTAs.

JBHI Journal 2025 Journal Article

A Ubiquitous Platform for Camera-Based Multi-Parameter Vital Signs Monitoring in Hospital ICUs: A Double-Center Clinical Study

• Huailei Lai
• Jia Huang
• Xin Tian
• Dongfang Yu
• Yonglong Ye
• Yongshen Zeng
• Yukai Huang
• Guowei Wang

Conventional physiological monitoring relies on multiple contact-based biomedical sensors, such as electrocardiograms, pulse oximeters, and blood pressure (BP) cuffs, which often necessitate the use of cumbersome sensors (e. g. , electrodes, patches, diodes) and extensive wiring. These conventional methods not only impose significant inconvenience on both caregivers and patients but also elevate the risk of patient infections. In this study, we introduce a novel non-contact, multi-parameter vital signs monitoring platform based on the red, green and near Infrared (RG-IR) spectral imaging system, which measures five critical patient parameters in the Intensive Care Unit (ICU), including heart rate (HR), heart rate variability (HRV), respiratory rate (RR), blood oxygen saturation (SpO $_{2}$ ) and BP. Clinical trials were conducted in the ICUs of two hospitals, involving 30 critically ill patients. The clinical outcomes indicate that our system achieves reasonable performance, with mean absolute errors of 1. 89 bpm for HR, 19. 04 ms for SDNN (standard deviation of normal-to-normal intervals), 0. 99 bpm for RR, 2. 75% for SpO $_{2}$ and 5. 67–8. 98 mmHg for BP, providing timely physiological information and marking a significant step toward contactless ICU monitoring. Additionally, the real-time performance of the core algorithms were validated on low-cost embedded chips, demonstrating the system's capability for edge computation and on-board patient monitoring. The proposed system has entered the registration process of the National Medical Products Administration (NMPA) as a Class II medical device (No. 2024007).

IJCAI Conference 2024 Conference Paper

Cross-Scale Domain Adaptation with Comprehensive Information for Pansharpening

• Meiqi Gong
• Hao Zhang
• Hebaixu Wang
• Jun Chen
• Jun Huang
• Xin Tian
• Jiayi Ma

Deep learning-based pansharpening methods typically use simulated data at the reduced-resolution scale for training. It limits their performance when generalizing the trained model to the full-resolution scale due to incomprehensive information utilization of panchromatic (PAN) images at the full-resolution scale and low generalization ability. In this paper, we adopt two targeted strategies to address the above two problems. On the one hand, we introduce a cross-scale comprehensive information capture module, which improves the information utilization of the original PAN image through fully-supervised reconstruction. On the other hand, we pioneer a domain adaptation strategy to tackle the problem of low generalization across different scales. Considering the instinct domain gap between different scales, we leverage the maximum mean discrepancy loss and the inherent pixel-level correlations between features at different scales to reduce the scale variance, thus boosting the generalization ability of our model. Experiments on various satellites demonstrate the superiority of our method over the state-of-the-arts in terms of information retention. Our code is publicly available at https: //github. com/Meiqi-Gong/SDIPS.

ECAI Conference 2024 Conference Paper

Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection

• Xingyu Ma
• Xin Tian
• Lingxiang Wu
• Xuepeng Wang
• Xueming Tang
• Jinqiao Wang

Text-to-SQL is a subtask in semantic parsing that has seen rapid progress with the evolution of Large Language Models (LLMs). However, LLMs face challenges due to hallucination issues and a lack of domain-specific database knowledge(such as table schema and cell values). As a result, they can make errors in generating table names, columns, and matching values to the correct columns in SQL statements. This paper introduces a method of knowledge injection to enhance LLMs’ ability to understand schema contents by incorporating prior knowledge. This approach improves their performance in Text-to-SQL tasks. Experimental results show that pre-training LLMs on domain-specific database knowledge and fine-tuning them on downstream Text-to-SQL tasks significantly improves the Execution Match (EX) and Exact Match (EM) metrics across various models. This effectively reduces errors in generating column names and matching values to the columns. Furthermore, the knowledge-injected models can be applied to many downstream Text-to-SQL tasks, demonstrating the generalizability of the approach presented in this paper.

JBHI Journal 2023 Journal Article

Remote Blood Oxygen Estimation From Videos Using Neural Networks

• Joshua Mathew
• Xin Tian
• Chau-Wai Wong
• Simon Ho
• Donald K. Milton
• Min Wu

Peripheral blood oxygen saturation (SpO $_{2}$ ) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO $_{2}$ before any obvious symptoms. Measuring an individual's SpO $_{2}$ without having to come into contact with the person can lower the risk of cross contamination and blood circulation problems. The prevalence of smartphones has motivated researchers to investigate methods for monitoring SpO $_{2}$ using smartphone cameras. Most prior schemes involving smartphones are contact-based: They require using a fingertip to cover the phone's camera and the nearby light source to capture reemitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO $_{2}$ estimation scheme using smartphone cameras. The scheme analyzes the videos of an individual's hand for physiological sensing, which is convenient and comfortable for users and can protect their privacy and allow for keeping face masks on. We design explainable neural network architectures inspired by the optophysiological models for SpO $_{2}$ measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO $_{2}$ measurement, showing the potential of the proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO $_{2}$ estimation performance.

TIST Journal 2020 Journal Article

Discovering Underlying Plans Based on Shallow Models

• Hankz Hankui Zhuo
• Yantian Zha
• Subbarao Kambhampati
• Xin Tian

Plan recognition aims to discover target plans (i.e., sequences of actions) behind observed actions, with history plan libraries or action models in hand. Previous approaches either discover plans by maximally “matching” observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing action models to best explain the observed actions, assuming that complete action models are available. In real-world applications, however, target plans are often not from plan libraries, and complete action models are often not available, since building complete sets of plans and complete action models are often difficult or expensive. In this article, we view plan libraries as corpora and learn vector representations of actions using the corpora; we then discover target plans based on the vector representations. Specifically, we propose two approaches, DUP and RNNPlanner, to discover target plans based on vector representations of actions. DUP explores the EM-style (Expectation Maximization) framework to capture local contexts of actions and discover target plans by optimizing the probability of target plans, while RNNPlanner aims to leverage long-short term contexts of actions based on RNNs (Recurrent Neural Networks) framework to help recognize target plans. In the experiments, we empirically show that our approaches are capable of discovering underlying plans that are not from plan libraries without requiring action models provided. We demonstrate the effectiveness of our approaches by comparing its performance to traditional plan recognition approaches in three planning domains. We also compare DUP and RNNPlanner to see their advantages and disadvantages.

AAMAS Conference 2016 Conference Paper

Discovering Underlying Plans Based on Distributed Representations of Actions

• Xin Tian
• Hankz Hankui Zhuo
• Subbarao Kambhampati