Author name cluster

Gaowei Zhang

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

SSTODE: Ocean-Atmosphere Physics-Informed Neural ODEs for Sea Surface Temperature Prediction

Zheng Jiang
Wei Wang
Gaowei Zhang
Yi Wang

Sea Surface Temperature (SST) is crucial for understanding upper-ocean thermal dynamics and ocean-atmosphere interactions, which have profound economic and social impacts. While data-driven models show promise in SST prediction, their black-box nature often limits interpretability and overlooks key physical processes. Recently, physics-informed neural networks have been gaining momentum but struggle with complex ocean-atmosphere dynamics due to 1) inadequate characterization of seawater movement (e.g., coastal upwelling) and 2) insufficient integration of external SST drivers (e.g., turbulent heat fluxes). To address these challenges, we propose SSTODE, a physics-informed Neural Ordinary Differential Equations (Neural ODEs) framework for SST prediction. First, we derive ODEs from fluid transport principles, incorporating both advection and diffusion to model ocean spatiotemporal dynamics. Through variational optimization, we recover a latent velocity field that explicitly governs the temporal dynamics of SST. Building upon ODE, we introduce an Energy Exchanges Integrator (EEI)-inspired by ocean heat budget equations-to account for external forcing factors. Thus, the variations in the components of these factors provide deeper insights into SST dynamics. Extensive experiments demonstrate that SSTODE achieves state-of-the-art performances in global and regional SST forecasting benchmarks. Furthermore, SSTODE visually reveals the impact of advection dynamics, thermal diffusion patterns, and diurnal heating-cooling cycles on SST evolution. These findings demonstrate the model's interpretability and physical consistency.

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NeurIPS Conference 2025 Conference Paper

CoralVQA: A Large-Scale Visual Question Answering Dataset for Coral Reef Image Understanding

Hongyong Han
Wei Wang
Gaowei Zhang
Mingjie Li
Yi Wang

Coral reefs are vital yet vulnerable ecosystems that require continuous monitoring to support conservation. While coral reef images provide essential information in coral monitoring, interpreting such images remains challenging due to the need for domain expertise. Visual Question Answering (VQA), powered by Large Vision-Language Models (LVLMs), has great potential in user-friendly interaction with coral reef images. However, applying VQA to coral imagery demands a dedicated dataset that addresses two key challenges: domain-specific annotations and multidimensional questions. In this work, we introduce CoralVQA, the first large-scale VQA dataset for coral reef analysis. It contains 12, 805 real-world coral images from 67 coral genera collected from 3 oceans, along with 277, 653 question-answer pairs that comprehensively assess ecological and health-related conditions. To construct this dataset, we develop a semi-automatic data construction pipeline in collaboration with marine biologists to ensure both scalability and professional-grade data quality. CoralVQA presents novel challenges and provides a comprehensive benchmark for studying vision-language reasoning in the context of coral reef images. By evaluating several state-of-the-art LVLMs, we reveal key limitations and opportunities. These insights form a foundation for future LVLM development, with a particular emphasis on supporting coral conservation efforts.

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AAAI Conference 2025 Conference Paper

Enhancing Vision-Language Models with Morphological and Taxonomic Knowledge: Towards Coral Recognition for Ocean Health

Hongyong Han
Wei Wang
Gaowei Zhang
Mingjie Li
Yi Wang

Coral reefs play a crucial role in marine ecosystems, offering a nutrient-rich environment and safe shelter for numerous marine species. Automated coral image recognition aids in monitoring ocean health at a scale without experts' manual effort. Recently, large vision-language models like CLIP have greatly enhanced zero-shot and low-shot classification capabilities for various visual tasks. However, these models struggle with fine-grained coral-related tasks due to a lack of specific knowledge. To bridge this gap, we compile a fine-grained coral image dataset consisting of 16,659 images with taxonomy labels (from Kingdom to Species), accompanied by morphology-specific text descriptions for each species. Based on the dataset, we propose CORAL-Adapter, integrating two complementary kinds of coral-specific knowledge (biological taxonomy and coral morphology) with general knowledge learned by CLIP. CORAL-Adapter is a simple yet powerful extension of CLIP with only a few parameter updates and can be used as a plug-and-play module with various CLIP-based methods. We show improvements in accuracy across diverse coral recognition tasks, e.g., recognizing corals unseen during training that are prone to bleaching or originate from different oceans.

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ECAI Conference 2025 Conference Paper

FenGePad-A Tangible Multi-Prompt Interactive Framework for Deep Dune Segmentation

Zheng Jiang
Haonan Kang
Zifeng Wu
Wei Wang 0353
Gaowei Zhang
Eerdun Hasi
Xiaohan Sun
Yi Wang 0013

Interactive segmentation has become critical for efficiently delineating dune boundaries from remote sensing landform images, enabling geographers to iteratively refine model predictions through minimal user guidance. However, geographers report two major challenges when working with existing tools: (1) handling segmentation around ambiguous dune boundaries forces geographers into dense, repetitive clicking, making the interaction tedious and reducing annotation efficiency; (2) conventional desktop-based annotation platforms mainly support sequential, isolated interactions, hindering the smooth, co-located collaboration necessary for dealing with difficult cases. We thus propose FenGePad, a tangible collaborative interactive segmentation framework. It supports flexible prompt types–clicks, polylines, and scribbles–designed to accommodate geographers’ diverse annotation preferences and improve annotation efficiency. To enhance model robustness and generalization, we introduce prompt generation strategies that simulate realistic annotation behaviors of geographers during training. Finally, we instantiate a tablet-based application supporting FenGePad’s tangible annotation and collaboration. Comprehensive experiments demonstrate that FenGePad achieves competitive segmentation performance while effectively improving annotation quality and collaborative efficiency. Our results demonstrate the promise of tangible interactive frameworks for applying deep learning in geographic research.

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EAAI Journal 2024 Journal Article

Dynamic personalized graph neural network with linear complexity for multivariate time series forecasting

ZhuoLin Li
Ziheng Gao
Gaowei Zhang
Jingjing Liu
LingYu Xu

Spatio-temporal graph neural networks (GNN) that model inter-variable dependencies with learned graph structures for multivariate time series forecasting have received attention due to their superior performance. However, existing methods exhibit quadratic complexity, limiting the GNN’s capability to handle a large number of variables. Moreover, the message-passing mechanism employed by spatio-temporal GNNs can be seen as a type of Laplacian smoothing, which weakens the inherent evolution patterns of nodes and overlooks the dynamic impact of a node’s own information and that of its neighbors on future outcomes. In this paper, we propose a novel approach called the Dynamic Personalized Graph Neural Network (DPGNN), which presents a graph learning framework with linear complexity. To facilitate linearization, we conceptualize the spatio-temporal GNN as an information flow from the source (raw node features) to the sink (new node features). And the flow conservation principle is employed to generate the information attention mechanism to effectively model the dependency between variables. To address the second issue, inspired by PageRank, we design a dynamic personalized graph convolution scheme that explicitly quantifies the impact of each node on the subsequent results based on the dynamic graph signals at each propagation step. Experimental results on six benchmark datasets showcase the superiority of our approach over state-of-the-art methods, including both pre-defined graph structures and graph learning techniques.

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ECAI Conference 2024 Conference Paper

FenGe-An Interactive Framework for Improving the Utility of Deep Dune Segmentation in Geographical Tasks

Zheng Jiang
Anqi Lu
Zifeng Wu
Wei Wang 0353
Gaowei Zhang
Eerdun Hasi
Yi Wang 0013

Segmenting dunes from remote sensing landforms images with deep vision models is promising by freeing geographers from manual visual interpretation tasks, making them more concentrated on the essential tasks in solving desertification challenges. However, geographers have reported that automated segmentation results may be not satisfactory though achieving high accuracy, implying there are potential gaps between pixel-level metrics and the utility in downstream geographic tasks. Therefore, pixel-wise metrics may be not proper in evaluating the deep dune segmentation in the geography domain, arising the necessity to develop domain-specific, human-centered measurements for deep dune segmentation. This paper first proposes a novel measurement based on geographers’ subjective judgments, which allows the evaluation of the alignment between deep dune segmentation models and geographical utility. We design an interactive framework integrating multiagent reinforcement learning (MARL) with geographers’ domain knowledge to improve models’ utility in the domain of geography. Our extensive experiments show that (1) our framework enables the interactive domain knowledge integration in the model-building process, and thus (2) the dune segmentation model better aligns with geographical utility, which ultimately improves the effectiveness of dune segmentation. We have deployed the framework with a number of geographers to support their various tasks including dune segmentation as a component. The results demonstrate our framework’s capabilities.

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EAAI Journal 2020 Journal Article

Cross-scale generative adversarial network for crowd density estimation from images

Gaowei Zhang
Yue Pan
Limao Zhang
Robert Lee Kong Tiong

This research develops a cross-scale convolutional spatial generative adversarial network (CSGAN), in order to estimate the crowd density from images accurately. It consists of two similar generators, one for the whole feature extraction, and the other for patch scale feature extraction. An encoder–decoder structure is employed to generate density maps from input images or patches. Additionally, a new objective function for crowd counting called cross-scale consistency pursuit containing an adversarial loss, L2 loss, perceptual loss, and consistency loss, is developed to make the generated density maps more realistic and closer to the ground truth. The effectiveness of the proposed CSGAN is verified in two public datasets. Results indicate that the new objective function is able to reach the most satisfying value of evaluation metrics in both the low-density and high-density crowd scenes when it is compared with other state-of-the-art methods on the test datasets. Moreover, the proposed CSGAN is more practical and flexible due to the smaller computational complexity. Its estimation capability will be significantly improved even in a small size of training data. Overall, this research contributes to the development of a novel computer vision approach together with a new objective function to generate density maps from cross-scale crowd images, enabling the counting process more accurately and efficiently.

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