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Mengzhu Wang

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19 papers

2 author rows

AAAI Conference 2026 Conference Paper

DeFT-LoRA: Decoupled and Fused Tuning with LoRA Experts for Universal Cross-Domain Retrieval

Ke Xu
Xiaozheng Shen
Shanshan Wang
Mengzhu Wang
Xun Yang

Universal Cross-Domain Retrieval (UCDR) aims to retrieve images across unseen domains and categories, a critical capability for real-world applications. While large-scale Vision-Language Models (VLMs) like CLIP offer strong zero-shot category generalization, they struggle with domain shifts. Existing methods often improve domain robustness at the cost of high computational overhead or by compromising the VLM's inherent knowledge. To address this, we propose Decoupled and Fused Tuning with LoRA (DeFT-LoRA), a novel and parameter-efficient framework that integrates Low-Rank Adaptation (LoRA) with a Mixture-of-Experts (MoE) mechanism. This approach resolves the intrinsic conflict between domain-invariant and domain-specific knowledge in a single adapter, enabling our model to construct a domain adapters for each input image. We propose a three-stage training strategy, which first learns a shared Base LoRA for domain-invariant features, then derives Domain-Specific Experts to capture specific styles, and finally fuses them dynamically with a lightweight gating network. Extensive experiments on three UCDR benchmarks demonstrate that DeFT-LoRA achieves comparable or superior performance to state-of-the-art methods while requiring only 1.46 percent of CLIP's image-encoder parameters and reducing computational overhead, thereby establishing an exceptional balance between accuracy and efficiency.

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YNIMG Journal 2026 Journal Article

Intrinsic divergence, repeatability, and distributional fingerprints of VFA, ME-SE, MDME, and MRF: a comparative evaluation of quantitative T1/T2 relaxometry in phantom and human brain at 3 T

Gaoyang Zhao
Jialiang Teng
Lina Zhang
Yueyue Zuo
Yanglei Wu
Mengzhu Wang
Yuncai Ran
Jingliang Cheng

Quantitative MRI (qMRI) relaxometry provides non-invasive biomarkers of brain microstructure, yet cross-method inconsistencies continue to hinder reliable comparison across studies and sites. To facilitate the clinical standardization of brain qMRI, this work systematically evaluated the accuracy and repeatability of three clinical brain T1/T2 relaxometry implementations under harmonized 3 T conditions: conventional variable flip-angle and multi-echo spin-echo (VFA/ME-SE), multi-dynamic multi-echo (MDME), and magnetic resonance fingerprinting (MRF). A standardized ISMRM/NIST system phantom and two healthy volunteer cohorts were examined. The phantom experiment quantified accuracy and bias; a multi-site “traveling brain” cohort (n = 12) assessed inter-scanner repeatability; and a single-site population cohort (n = 38) characterized distributional “fingerprints” and biological sensitivity using a high-precision AI-based segmentation pipeline. In phantom validation, MRF achieved the highest accuracy and stability across the physiological range, whereas ME-SE exhibited precision loss at long T2. In vivo, all methods demonstrated excellent inter-site repeatability with coefficients of variation (CVs) below 5%, where MRF achieved the highest stability for T1 (CV = 0. 61%) and MDME yielded the highest stability for T2 (CV = 0. 42%). However, substantial intrinsic discrepancies persisted: relative to MRF, VFA systematically overestimated T1 (particularly in deep gray matter), while MDME underestimated T1. For T2, a fundamental baseline shift was observed, with ME-SE and MDME yielding values nearly double those of MRF in iron-rich regions. Supplementary investigations confirmed these offsets arise from proprietary reconstruction and signal encoding differences (transient-state vs. steady-state) rather than simple protocol constraints. Biologically, the optimized segmentation pipeline enhanced sensitivity to age-related trends, revealing significant T1 shortening across all methods, while VFA alone exhibited significant sex-bias confounding not observed in MRF or MDME. These findings provide quantitative benchmarks and practical guidance for standardizing brain qMRI relaxometry across acquisition methods, scanners, and research sites.

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AAAI Conference 2026 Conference Paper

Learning to Cluster Rare Cell Types: Implicit Semantic Data Augmentation for Spatial Multi-modal Omics Analysis

Daixian Liu
Hau-Sing So
Haoran Chen
Jiao Li
Shanshan Wang
Mengzhu Wang
Jingcai Guo

Spatial multi-modal omics technologies have transformed biological research by enabling the simultaneous profiling of gene expression, protein abundance, and chromatin accessibility within their native spatial contexts. Despite these advances, accurately clustering rare cell types remains a major challenge due to data sparsity, high dimensionality, and limited annotated samples. While Graph Neural Networks (GNNs) have shown potential in modeling spatial omics data, their effectiveness is often constrained by the use of fixed K-nearest neighbor (KNN) graph structures, which fail to capture latent semantic relationships masked by sequencing noise. To overcome these limitations, we propose CRCT (Clustering Rare Cell Types): a novel framework that combines Implicit Semantic Data Augmentation (ISDA) with adaptive graph learning for spatial multi-modal omics analysis. Unlike traditional augmentation strategies that generate explicit synthetic samples, CRCT operates in the deep feature space by dynamically estimating intra-class covariance matrices and implicitly perturbing features along semantically meaningful directions. This enables effective augmentation for rare cell populations while preserving biological fidelity. Extensive experiments across four real-world datasets (HLN, MB, Stereo‑CITE‑seq, and SPOTS) and one synthetic benchmark demonstrate the state-of-the-art performance of CRCT, achieving improvements of up to +1.7 NMI and +7.8 ARI over strong baseline methods.

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AAAI Conference 2026 Conference Paper

Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning

Yingxu Wang
Mengzhu Wang
Zhichao Huang
Suyu Liu
Nan Yin

Graph Domain Adaptation (GDA) facilitates knowledge transfer from labeled source graphs to unlabeled target graphs by learning domain-invariant representations, which is essential in applications such as molecular property prediction and social network analysis. However, most existing GDA methods rely on the assumption of clean source labels, which rarely holds in real-world scenarios where annotation noise is pervasive. This label noise severely impairs feature alignment and degrades adaptation performance under domain shifts. To address this challenge, we propose Nested Graph Pseudo-Label Refinement (NeGPR), a novel framework tailored for graph-level domain adaptation with noisy labels. NeGPR first pretrains dual branches, i.e., semantic and topology branches, by enforcing neighborhood consistency in the feature space, thereby reducing the influence of noisy supervision. To bridge domain gaps, NeGPR employs a nested refinement mechanism in which one branch selects high-confidence target samples to guide the adaptation of the other, enabling progressive cross-domain learning. Furthermore, since pseudo-labels may still contain noise and the pre-trained branches are already overfitted to the noisy labels in the source domain, NeGPR incorporates a noise-aware regularization strategy. This regularization is theoretically proven to mitigate the adverse effects of pseudo-label noise, even under the presence of source overfitting, thus enhancing the robustness of the adaptation process. Extensive experiments on benchmark datasets demonstrate that NeGPR consistently outperforms state-of-the-art methods under severe label noise.

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IJCAI Conference 2025 Conference Paper

Coupling Category Alignment for Graph Domain Adaptation

Nan Yin
Xiao Teng
Zhiguang Cao
Mengzhu Wang

Graph domain adaptation (GDA), which transfers knowledge from a labeled source domain to an unlabeled target graph domain, attracts considerable attention in numerous fields. However, existing methods commonly employ message-passing neural networks (MPNNs) to learn domain-invariant representations by aligning the entire domain distribution, inadvertently neglecting category-level distribution alignment and potentially causing category confusion. To address the problem, we propose an effective framework named Coupling Category Alignment (CoCA) for GDA, which effectively addresses the category alignment issue with theoretical guarantees. CoCA incorporates a graph convolutional network branch and a graph kernel network branch, which explore graph topology in implicit and explicit manners. To mitigate category-level domain shifts, we leverage knowledge from both branches, iteratively filtering highly reliable samples from the target domain using one branch and fine-tuning the other accordingly. Furthermore, with these reliable target domain samples, we incorporate the coupled branches into a holistic contrastive learning framework. This framework includes multi-view contrastive learning to ensure consistent representations across the dual branches, as well as cross-domain contrastive learning to achieve category-level domain consistency. Theoretically, we establish a sharper generalization bound, which ensures the effectiveness of category alignment. Extensive experiments on benchmark datasets validate the superiority of the proposed CoCA compared with baselines.

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IJCAI Conference 2025 Conference Paper

ESBN: Estimation Shift of Batch Normalization for Source-free Universal Domain Adaptation

Jiao Li
Houcheng Su
Bingli Wang
Yuandong Min
Mengzhu Wang
Nan Yin
Shanshan Wang
Jingcai Guo

Domain adaptation (DA) is crucial for transferring models trained in one domain to perform well in a different, often unseen domain. Traditional methods, including unsupervised domain adaptation (UDA) and source-free domain adaptation (SFDA), have made significant progress. However, most existing DA methods rely heavily on Batch Normalization (BN) layers, which are not optimal in source-free settings, where the source domain is unavailable for comparison. In this study, we propose a novel method, ESBN, which addresses the challenge of domain shift by adjusting the placement of normalization layers and replacing BN with Batch-free Normalization (BFN). Unlike BN, BFN is less dependent on batch statistics and provides more robust feature representations through instance-specific statistics. We systematically investigate the effects of different BN layer placements across various network configurations and demonstrate that selective replacement with BFN improves generalization performance. Extensive experiments on multiple domain adaptation benchmarks show that our approach outperforms state-of-the-art methods, particularly in challenging scenarios such as Open-Partial Domain Adaptation (OPDA).

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IJCAI Conference 2025 Conference Paper

Exploring Transferable Homogenous Groups for Compositional Zero-Shot Learning

Zhijie Rao
Jingcai Guo
Miaoge Li
Yang Chen
Mengzhu Wang

Conditional dependency present one of the trickiest problems in Compositional Zero-Shot Learning, leading to significant property variations of the same state (object) across different objects (states). To address this problem, existing approaches often adopt either all-to-one or one-to-one representation paradigms. However, these extremes create an imbalance in the seesaw between transferability and discriminability, favoring one at the expense of the other. Comparatively, humans are adept at analogizing and reasoning in a hierarchical clustering manner, intuitively grouping categories with similar properties to form cohesive concepts. Motivated by this, we propose Homogeneous Group Representation Learning (HGRL), a new perspective formulates state (object) representation learning as multiple homogeneous sub-group representation learning. HGRL seeks to achieve a balance between semantic transferability and discriminability by adaptively discovering and aggregating categories with shared properties, learning distributed group centers that retain group-specific discriminative features. Our method integrates three core components designed to simultaneously enhance both the visual and prompt representation capabilities of the model. Extensive experiments on three benchmark datasets validate the effectiveness of our method. Code is available at https: //github. com/zjrao/HGRL.

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IJCAI Conference 2025 Conference Paper

Gaussian Mixture Model for Graph Domain Adaptation

Mengzhu Wang
Wenhao Ren
Yu Zhang
Yanlong Fan
Dianxi Shi
Luoxi Jing
Nan Yin

Unsupervised domain adaptation (UDA) has been widely studied with the goal of transferring knowledge from a label-rich source domain to a related but unlabeled target domain. Most UDA techniques achieve this by reducing the feature discrepancies between the two domains to learn domain-invariant feature representations. While domain-invariant feature representations can reduce the differences between the source and target domains, excessively simplifying these differences may cause the model to overlook important domain-specific features, resulting in a decline in transfer learning effectiveness. To address this issue, this paper proposes a novel Gaussian Mixture Model for graph domain adaptation (GMM). This model effectively reduces the distributional bias between the source and target domains by modeling the distribution differences on a graph structure. GMM leverages the local structural information of the graph and the clustering capability of the Gaussian mixture model to automatically learn the latent mapping relationships between the source and target domains. To the best of our knowledge, this is the first work to introduce a Gaussian mixture model into UDA. Extensive experimental results on three standard benchmarks demonstrate that the proposed GMM algorithm outperforms state-of-the-art unsupervised domain adaptation methods in terms of performance.

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ICML Conference 2025 Conference Paper

GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation

Mengzhu Wang
Houcheng Su
Jiao Li
Chuan Li
Nan Yin
Li Shen 0008
Jingcai Guo

Semi-supervised learning (SSL) has made notable advancements in medical image segmentation (MIS), particularly in scenarios with limited labeled data and significantly enhancing data utilization efficiency. Previous methods primarily focus on complex training strategies to utilize unlabeled data but neglect the importance of graph structural information. Different from existing methods, we propose a graph-based clustering for semi-supervised medical image segmentation (GraphCL) by jointly modeling graph data structure in a unified deep model. The proposed GraphCL model enjoys several advantages. Firstly, to the best of our knowledge, this is the first work to model the data structure information for semi-supervised medical image segmentation (SSMIS). Secondly, to get the clustered features across different graphs, we integrate both pairwise affinities between local image features and raw features as inputs. Extensive experimental results on three standard benchmarks show that the proposed GraphCL algorithm outperforms state-of-the-art semi-supervised medical image segmentation methods.

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IJCAI Conference 2025 Conference Paper

In-Context Meta LoRA Generation

Yihua Shao
Minxi Yan
Yang Liu
Siyu Chen
Wenjie Chen
Xinwei Long
Ziyang Yan
Lei Li

Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning. However, in scenarios that involve multiple tasks, training a separate LoRA model for each one results in considerable inefficiency in terms of storage and inference. Moreover, existing parameter generation methods fail to capture the correlations among these tasks, making multi-task LoRA parameter generation challenging. To address these limitations, we propose In-Context Meta LoRA (ICM-LoRA), a novel approach that efficiently achieves task-specific customization of large language models (LLMs). Specifically, we use training data from all tasks to train a tailored generator, Conditional Variational Autoencoder (CVAE). CVAE takes task descriptions as inputs and produces task-aware LoRA weights as outputs. These LoRA weights are then merged with LLMs to create task-specialized models without the need for additional fine-tuning. Furthermore, we utilize in-context meta-learning for knowledge enhancement and task mapping, to capture the relationship between tasks and parameter distributions. As a result, our method achieves more accurate LoRA parameter generation for diverse tasks using CVAE. ICM-LoRA enables more accurate LoRA parameter reconstruction than current parameter reconstruction methods and is useful for implementing task-specific enhancements of LoRA parameters. At the same time, our method occupies 283MB, only 1% storage compared with the original LoRA. The code is available at https: //github. com/YihuaJerry/ICM-LoRA.

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ECAI Conference 2025 Conference Paper

Pano3R: Training Free Panoramic 3D Reconstruction

Shiming Song 0003
Yongjun Zhang 0006
Yuanze Wang
Mengzhu Wang
Yuetian Wang
Zhuojing Tian
Jinming Song
Dianxi Shi

Panoramic 3D reconstruction is essential for immersive scene understanding in robotics, AR, and autonomous driving. However, most existing methods are designed for pinhole images and generalize poorly to 360° inputs due to the scarcity of panoramic training data and the high cost of retraining. We present Pano3R, the first training-free framework for panoramic 3D reconstruction that adapts existing pinhole-based models without any retraining. Pano3R consists of two stages. Specifically, the pre-processing stage applies a position-aware pairing strategy to decompose each panorama into a minimal set of perspective views. These views are selected to ensure sufficient co-visible regions while minimizing the number of projections. The test-time optimization stage incorporates a pose-prior-guided global alignment strategy to improve global consistency and mitigate accumulated errors. Our method enables accurate 360° reconstruction under both single- and multi-view input conditions. Extensive experiments demonstrate that Pano3R consistently improves reconstruction accuracy and pose estimation quality, establishing a strong and practical benchmark for training-free panoramic 3D reconstruction.

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IJCAI Conference 2025 Conference Paper

Wave-wise Discriminative Tracking by Phase-Amplitude Separation, Augmentation and Mixture

Huibin Tan
Mingyu Cao
Kun Hu
Xihuai He
Zhe Wang
Hao Li
Long Lan
Mengzhu Wang

Distinguishing key features in complex visual tasks is challenging. A novel approach treats image patches (tokens) as waves. By using both phase and amplitude, it captures richer semantics and specific invariances compared to pixel-based methods, and allows for feature fusion across regions for a holistic image representation. Based on this, we propose the Wave-wise Discriminative Transformer Tracker (WDT). During tracking, WDT represents features via phase-amplitude separation, enhancement, and mixture. First, we designed a Mutual Exclusive Phase-Amplitude Extractor (MEPAE) to separate phase and amplitude features with distinct semantics, representing spatial target info and background brightness respectively. Then, Wave-wise Feature Augmentation is carried out with two submodules: Phase-Amplitude Feature Augmentation and Mixture. The augmentation module disrupts the separated features in the same batch, and the mixture module recombines them to generate positive and negative waves. The original features are aggregated into the original wave. Positive waves have the same phase but different amplitudes, and negative waves have different phase components. Finally, self-supervised and tracking-supervised losses guide the global and local representation learning for original, positive, and negative waves, enhancing wave-level discrimination. Experiments on five benchmarks prove the effectiveness of our method.

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AAAI Conference 2024 Conference Paper

Correlation Matching Transformation Transformers for UHD Image Restoration

Cong Wang
Jinshan Pan
Wei Wang
Gang Fu
Siyuan Liang
Mengzhu Wang
Xiao-ming Wu
Jun Liu

This paper proposes UHDformer, a general Transformer for Ultra-High-Definition (UHD) image restoration. UHDformer contains two learning spaces: (a) learning in high-resolution space and (b) learning in low-resolution space. The former learns multi-level high-resolution features and fuses low-high features and reconstructs the residual images, while the latter explores more representative features learning from the high-resolution ones to facilitate better restoration. To better improve feature representation in low-resolution space, we propose to build feature transformation from the high-resolution space to the low-resolution one. To that end, we propose two new modules: Dual-path Correlation Matching Transformation module (DualCMT) and Adaptive Channel Modulator (ACM). The DualCMT selects top C/r (r is greater or equal to 1 which controls the squeezing level) correlation channels from the max-pooling/mean-pooling high-resolution features to replace low-resolution ones in Transformers, which can effectively squeeze useless content to improve the feature representation in low-resolution space to facilitate better recovery. The ACM is exploited to adaptively modulate multi-level high-resolution features, enabling to provide more useful features to low-resolution space for better learning. Experimental results show that our UHDformer reduces about ninety-seven percent model sizes compared with most state-of-the-art methods while significantly improving performance under different training sets on 3 UHD image restoration tasks, including low-light image enhancement, image dehazing, and image deblurring. The source codes will be made available at https://github.com/supersupercong/UHDformer.

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ICLR Conference 2024 Conference Paper

DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption

Nan Yin
Mengzhu Wang
Zhenghan Chen
Li Shen 0008
Huan Xiong
Bin Gu 0001
Xiao Luo 0001

Recently, numerous graph neural network methods have been developed to tackle domain shifts in graph data. However, these methods presuppose that unlabeled target graphs belong to categories previously seen in the source domain. This assumption could not hold true for in-the-wild target graphs. In this paper, we delve deeper to explore a more realistic problem open-set graph domain adaptation. Our objective is to not only identify target graphs from new categories but also accurately classify remaining target graphs into their respective categories under domain shift and label scarcity. To solve this challenging problem, we introduce a new method named Dual Structured Exploration with Mixup (DREAM). DREAM incorporates a graph-level representation learning branch as well as a subgraph-enhanced branch, which jointly explores graph topological structures from both global and local viewpoints. To maximize the use of unlabeled target graphs, we train these two branches simultaneously using posterior regularization to enhance their inter-module consistency. To accommodate the open-set setting, we amalgamate dissimilar samples to generate virtual unknown samples belonging to novel classes. Moreover, to alleviate domain shift, we establish a k nearest neighbor-based graph-of-graphs and blend multiple neighbors of each sample to produce cross-domain virtual samples for inter-domain consistency learning. Extensive experiments validate the effectiveness of the proposed DREAM in comparison to various state-of-the-art approaches in different settings.

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AAAI Conference 2024 Conference Paper

Dynamic Spiking Graph Neural Networks

Nan Yin
Mengzhu Wang
Zhenghan Chen
Giulia De Masi
Huan Xiong
Bin Gu

The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented by graphs. However, as a common problem, dynamic graph representation learning faces challenges such as high complexity and large memory overheads. Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which overlooks graph structure information and leads to the loss of details during propagation. Additionally, optimizing dynamic spiking models typically requires the propagation of information across time steps, which increases memory requirements. To address these challenges, we present a framework named Dynamic Spiking Graph Neural Networks (Dy-SIGN). To mitigate the information loss problem, Dy-SIGN propagates early-layer information directly to the last layer for information compensation. To accommodate the memory requirements, we apply the implicit differentiation on the equilibrium state, which does not rely on the exact reverse of the forward computation. While traditional implicit differentiation methods are usually used for static situations, Dy-SIGN extends it to the dynamic graph setting. Extensive experiments on three large-scale real-world dynamic graph datasets validate the effectiveness of Dy-SIGN on dynamic node classification tasks with lower computational costs.

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NeurIPS Conference 2024 Conference Paper

Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

Yang Dai
Oubo Ma
Longfei Zhang
Xingxing Liang
Shengchao Hu
Mengzhu Wang
Shouling Ji
Jincai Huang

Transformer-based trajectory optimization methods have demonstrated exceptional performance in offline Reinforcement Learning (offline RL). Yet, it poses challenges due to substantial parameter size and limited scalability, which is particularly critical in sequential decision-making scenarios where resources are constrained such as in robots and drones with limited computational power. Mamba, a promising new linear-time sequence model, offers performance on par with transformers while delivering substantially fewer parameters on long sequences. As it remains unclear whether Mamba is compatible with trajectory optimization, this work aims to conduct comprehensive experiments to explore the potential of Decision Mamba (dubbed DeMa) in offline RL from the aspect of data structures and essential components with the following insights: (1) Long sequences impose a significant computational burden without contributing to performance improvements since DeMa's focus on sequences diminishes approximately exponentially. Consequently, we introduce a Transformer-like DeMa as opposed to an RNN-like DeMa. (2) For the components of DeMa, we identify the hidden attention mechanism as a critical factor in its success, which can also work well with other residual structures and does not require position embedding. Extensive evaluations demonstrate that our specially designed DeMa is compatible with trajectory optimization and surpasses previous methods, outperforming Decision Transformer (DT) with higher performance while using 30\% fewer parameters in Atari, and exceeding DT with only a quarter of the parameters in MuJoCo.

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AAAI Conference 2024 Conference Paper

Sharpness-Aware Model-Agnostic Long-Tailed Domain Generalization

Houcheng Su
Weihao Luo
Daixian Liu
Mengzhu Wang
Jing Tang
Junyang Chen
Cong Wang
Zhenghan Chen

Domain Generalization (DG) aims to improve the generalization ability of models trained on a specific group of source domains, enabling them to perform well on new, unseen target domains. Recent studies have shown that methods that converge to smooth optima can enhance the generalization performance of supervised learning tasks such as classification. In this study, we examine the impact of smoothness-enhancing formulations on domain adversarial training, which combines task loss and adversarial loss objectives. Our approach leverages the fact that converging to a smooth minimum with respect to task loss can stabilize the task loss and lead to better performance on unseen domains. Furthermore, we recognize that the distribution of objects in the real world often follows a long-tailed class distribution, resulting in a mismatch between machine learning models and our expectations of their performance on all classes of datasets with long-tailed class distributions. To address this issue, we consider the domain generalization problem from the perspective of the long-tail distribution and propose using the maximum square loss to balance different classes which can improve model generalizability. Our method's effectiveness is demonstrated through comparisons with state-of-the-art methods on various domain generalization datasets. Code: https://github.com/bamboosir920/SAMALTDG.

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ICML Conference 2023 Conference Paper

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification

Nan Yin
Li Shen 0008
Mengzhu Wang
Long Lan
Zeyu Ma 0001
Chong Chen 0002
Xian-Sheng Hua 0001
Xiao Luo 0001

Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to explore additional labeled graphs to enhance unsupervised learning on the target domain. However, how to apply GNNs to domain adaptation remains unsolved owing to the insufficient exploration of graph topology and the significant domain discrepancy. In this paper, we propose Coupled Contrastive Graph Representation Learning (CoCo), which extracts the topological information from coupled learning branches and reduces the domain discrepancy with coupled contrastive learning. CoCo contains a graph convolutional network branch and a hierarchical graph kernel network branch, which explore graph topology in implicit and explicit manners. Besides, we incorporate coupled branches into a holistic multi-view contrastive learning framework, which not only incorporates graph representations learned from complementary views for enhanced understanding, but also encourages the similarity between cross-domain example pairs with the same semantics for domain alignment. Extensive experiments on popular datasets show that our CoCo outperforms these competing baselines in different settings generally.

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NeurIPS Conference 2023 Conference Paper

PromptRestorer: A Prompting Image Restoration Method with Degradation Perception

Cong Wang
Jinshan Pan
Wei Wang
Jiangxin Dong
Mengzhu Wang
Yakun Ju
Junyang Chen

We show that raw degradation features can effectively guide deep restoration models, providing accurate degradation priors to facilitate better restoration. While networks that do not consider them for restoration forget gradually degradation during the learning process, model capacity is severely hindered. To address this, we propose a Prompting image Restorer, termed as PromptRestorer. Specifically, PromptRestorer contains two branches: a restoration branch and a prompting branch. The former is used to restore images, while the latter perceives degradation priors to prompt the restoration branch with reliable perceived content to guide the restoration process for better recovery. To better perceive the degradation which is extracted by a pre-trained model from given degradation observations, we propose a prompting degradation perception modulator, which adequately considers the characters of the self-attention mechanism and pixel-wise modulation, to better perceive the degradation priors from global and local perspectives. To control the propagation of the perceived content for the restoration branch, we propose gated degradation perception propagation, enabling the restoration branch to adaptively learn more useful features for better recovery. Extensive experimental results show that our PromptRestorer achieves state-of-the-art results on 4 image restoration tasks, including image deraining, deblurring, dehazing, and desnowing.

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