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Wei Huang, Lei Zhang, Zizhou Wang, Yan Wang

Medical image segmentation provides detailed understanding and aids in diagnosis, treatment planning, and monitoring of diseases. Due to the high cost of acquiring labeled data in the field of medical image analysis, semi-supervised segmentation methods have garnered increasing attention. Benefiting from their simplicity and effectiveness, consistency regularization-based methods have emerged as a significant research focus by utilizing perturbations. However, existing methods typically consider perturbation strategies from only a single perspective: either instance perturbation or model perturbation, thus ignoring the potential benefit of effectively combining both. In response, we propose a unified perturbation framework named GapMatch, which bridges instance and model perturbations to broaden the perturbation space and employs dual perturbation to impose consistency regularization on the model. Specifically, GapMatch involves using instance perturbation to update the decision boundary and model perturbation to further optimize it. These two steps mutually reinforce each other in an iterative manner, effectively pushing the decision boundary towards low-density regions while maximizing the class margin. Extensive experimental results on two popular medical image benchmarks demonstrate the effectiveness and generality of the proposed method.

Sida Huang, Hongyuan Zhang, Xuelong Li

With the advancement of pre-trained vision-language (VL) models, enhancing the alignment between visual and linguistic modalities in downstream tasks has emerged as a critical challenge. Different from existing fine-tuning methods that add extra modules to these two modalities, we investigate whether the frozen model can be fine-tuned by customized noise. Our approach is motivated by the scientific study of beneficial noise, namely Positive-incentive Noise (Pi-noise) , which quantitatively analyzes the impact of noise. It therefore implies a new scheme to learn beneficial noise distribution that can be employed to fine-tune VL models. Focusing on few-shot classification tasks based on CLIP, we reformulate the inference process of CLIP and apply variational inference, demonstrating how to generate Pi-noise towards visual and linguistic modalities. Then, we propose Positive-incentive Noise Injector (PiNI), which can fine-tune CLIP via injecting noise into both visual and text encoders. Since the proposed method can learn the distribution of beneficial noise, we can obtain more diverse embeddings of vision and language to better align these two modalities for specific downstream tasks within limited computational resources. We evaluate different noise incorporation approaches and network architectures of PiNI. The evaluation across 11 datasets demonstrates its effectiveness.

Shiluo Huang, Yining Jin, Wei Jin, Ying Mu

Infrared (IR) spectroscopy is a fundamental technique in analytical chemistry. Recently, deep learning (DL) has drawn great interest as the modeling method of infrared spectral data. However, unlike vision or language tasks, IR spectral data modeling is faced with the problem of calibration transfer and has distinctive characteristics. Introducing the prior knowledge of IR spectroscopy could guide the DL methods to learn representations aligned with the domain-invariant characteristics of spectra, and thus improve the performance. Despite such potential, there is a notable absence of DL methods that incorporate such inductive bias. To this end, we propose Analytical-Chemistry-Informed Transformer (ACT) with two modules informed by the field knowledge in analytical chemistry. First, ACT includes learnable spectral processing inspired by chemometrics, which comprises spectral pre-processing, tokenization, and post-processing. Second, a straightforward yet effective representation learning mechanism, namely spectral-attention, is incorporated into ACT. Spectral-attention utilizes the intra-spectral and inter-spectral correlations to extract spectral representations. Empirical results show that ACT has achieved competitive results in 9 analytical tasks covering applications across pharmacy, chemistry, and agriculture. Compared with existing networks, ACT reduces the root mean square error of prediction (RMSEP) by more than 20% in calibration transfer tasks. These results indicate that DL methods in IR spectroscopy could benefit from the integration of prior knowledge in analytical chemistry.

Jintao Huang, Yiu-ming Cheung, Chi-man Vong, Wenbin Qian

Partial label learning (PLL) is a complicated weakly supervised multi-classification task compounded by class imbalance. Currently, existing methods only rely on inter-class pseudo-labeling from inter-class features, often overlooking the significant impact of the intra-class imbalanced features combined with the inter-class. To address these limitations, we introduce Granular Ball Representation for Imbalanced PLL (GBRIP), a novel framework for imbalanced PLL. GBRIP utilizes coarse-grained granular ball representation and multi-center loss to construct a granular ball-based feature space through unsupervised learning, effectively capturing the feature distribution within each class. GBRIP mitigates the impact of confusing features by systematically refining label disambiguation and estimating imbalance distributions. The novel multi-center loss function enhances learning by emphasizing the relationships between samples and their respective centers within the granular balls. Extensive experiments on standard benchmarks demonstrate that GBRIP outperforms existing state-of-the-art methods, offering a robust solution to the challenges of imbalanced PLL.

Huiqun Huang, Sihong He, Fei Miao

Trajectory prediction models that can infer both future trajectories and their associated uncertainties of the target vehicles is crucial for safe and robust navigation and path planning of autonomous vehicles. However, the majority of existing trajectory prediction models have neither considered reducing the uncertainty as one objective during the training stage nor provided reliable uncertainty quantification during inference stage, especially under potential distribution shift. Therefore, in this paper, we propose the Conformal Uncertainty Quantification under Distribution Shift framework, CUQDS, to quantify the uncertainty of the predicted trajectories of existing trajectory prediction models under potential data distribution shift, while improving the prediction accuracy of the models and reducing the estimated uncertainty during the training stage. Specifically, CUQDS includes 1) a learning-based Gaussian process regression module that models the output distribution of the base model (any existing trajectory prediction neural networks) and reduces the estimated uncertainty by an additional loss term, and 2) a statistical-based Conformal P control module to calibrate the estimated uncertainty from the Gaussian process regression module in an online setting under potential distribution shift between training and testing data. Experimental results on various state-of-the-art methods using benchmark motion forecasting datasets demonstrate the effectiveness of our proposed design.

Haojian Huang, Chuanyu Qin, Zhe Liu, Kaijing Ma, Jin Chen, Han Fang, Chao Ban, Hao Sun, Zhongjiang He

Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process. While Evidential Deep Learning (EDL) has been effective in addressing view uncertainty, existing methods predominantly rely on the Dempster-Shafer combination rule, which is sensitive to conflicting evidence and often neglects the critical role of neighborhood structures within multi-view data. To address these limitations, we propose a Trusted Unified Feature-NEighborhood Dynamics (TUNED) model for robust MVC. This method effectively integrates local and global feature-neighborhood (F-N) structures for robust decision-making. Specifically, we begin by extracting local F-N structures within each view. To further mitigate potential uncertainties and conflicts in multi-view fusion, we employ a selective Markov random field that adaptively manages cross-view neighborhood dependencies. Additionally, we employ a shared parameterized evidence extractor that learns global consensus conditioned on local F-N structures, thereby enhancing the global integration of multi-view features. Experiments on benchmark datasets show that our method improves accuracy and robustness over existing approaches, particularly in scenarios with high uncertainty and conflicting views.

Haocheng Huang, Jiaxin Chen, Jinyang Guo, Ruiyi Zhan, Yunhong Wang

Diffusion models have achieved remarkable success in the image and video generation tasks. Nevertheless, they often require a large amount of memory and time overhead during inference, due to the complex network architecture and considerable number of timesteps for iterative diffusion. Recently, the post-training quantization (PTQ) technique has proved a promising way to reduce the inference cost by quantizing the float-point operations to low-bit ones. However, most of them fail to tackle with the large variations in the distribution of activations across distinct channels and timesteps, as well as the inconsistent of input between quantization and inference on diffusion models, thus leaving much room for improvement. To address the above issues, we propose a novel method dubbed Timestep-Channel Adaptive Quantization for Diffusion Models (TCAQ-DM). Specifically, we develop a timestep-channel joint reparameterization (TCR) module to balance the activation range along both the timesteps and channels, facilitating the successive reconstruction procedure. Subsequently, we employ a dynamically adaptive quantization (DAQ) module that mitigate the quantization error by selecting an optimal quantizer for each post-Softmax layers according to their specific types of distributions. Moreover, we present a progressively aligned reconstruction (PAR) strategy to mitigate the bias caused by the input mismatch. Extensive experiments on various benchmarks and distinct diffusion models demonstrate that the proposed method substantially outperforms the state-of-the-art approaches in most cases, especially yielding comparable FID metrics to the full precision model on CIFAR-10 in the W6A6 setting, while enabling generating available images in the W4A4 settings.

Chengxiang Huang, Bingyan Liu

Federated Learning (FL) is a distributed approach that enables collaborative model training while safeguarding client data privacy. Nevertheless, FL encounters difficulties due to statistical heterogeneity from the varied data distributions across numerous clients, which can affect overall efficiency and performance. Existing state-of-the-art FL methods often concentrate on optimizing interactions between clients, neglecting the potential insights from individual clients during training. Additionally, these approaches generally assume that every period of training has an equal impact on the final model's performance. To address these issues, this paper introduces a novel method, PA3Fed, which conducts period-aware adaptive aggregation for improved federated learning. The key idea is to identify the most critical periods, i.e., those with the highest information content and entropy, where we leverages each client's own performance variations during training for adaptive aggregation. Furthermore, because it operates independently of inter-client optimization approaches, it can be easily incorporated into other baselines for improved performance. Experimental results show that our method improves accuracy by up to 15% and significantly enhances stability.

Chao Huang, Yushu Shi, Waikeung Wong, Chengliang Liu, Wei Wang, Zhihua Wang, Jie Wen

Medical image segmentation provides useful information about the shape and size of organs, which is beneficial for improving diagnosis, analysis, and treatment. Despite traditional deep learning-based models can extract domain-specific knowledge, they face a generalization bottleneck due to the limited embedded knowledge scope. Vision foundation models have been demonstrated to be effective in extracting generalizable knowledge, but they cannot extract domain-specific knowledge without fine-tuning. In this work, we propose a novel multi-view evidential learning-based framework, which can extract both domain-specific and generalizable knowledge from multi-view features by combining the advantages of traditional and vision foundation models. Specifically, a novel multi-view state space model (MV-SSM) is designed to extract task-related knowledge while removing redundant information within multi-view features. The proposed MV-SSM utilizes Mamba, a state space model, to model cross-view contextual dependencies between domain-specific and generalizable features. Additionally, evidential learning is adopted to quantify the segmentation uncertainty of the model for boundary. In special, variational Dirichlet is introduced to characterize the distribution of the result probabilities, parameterized with collected evidence to quantify uncertainty. As a result, the model can reduce the segmentation uncertainties of boundaries by optimizing the parameters of the Dirichlet distribution. Experimental results on three datasets show that our method obtains superior segmentation performance.

Baichuan Huang, Amir Aminifar

Forward-only algorithms offer a promising memory-efficient alternative to Backpropagation (BP) for on-device learning. However, state-of-the-art forward-only algorithms, e.g., Forward-Forward (FF), still require a substantial amount of memory during the training process, often exceeding the limits of mobile edge and Internet of Things (IoT) devices. At the same time, existing memory-optimization techniques, e.g., binarizing parameters and activations, are mainly designed for BP, hence significantly degrading the classification performance when applied to state-of-the-art forward-only algorithms. In this paper, we propose a memory-efficient forward-only algorithm called TinyFoA, to reduce dynamic memory overhead in the training process. Our TinyFoA optimizes the memory efficiency not only by layer-wise training but also by partially updating each layer, as well as by binarizing the weights and the activations. We extensively evaluate our proposed TinyFoA against BP and other forward-only algorithms and demonstrate its effectiveness and superiority compared to state-of-the-art forward-only algorithms in terms of classification performance and training memory overhead, reducing the memory overheads by an order of magnitude.

Yang Hua, Tianyang Xu, Xiaoning Song, Zhenhua Feng, Rui Wang, Wenjie Zhang, Xiaojun Wu

Drug Target Interaction (DTI) prediction has witnessed promising performance boosts accompanied by advanced multimodal feature extraction. However, existing approaches suffer from two main difficulties. First, the complex protein structures cannot be well represented by current protein-sequence-based feature extractors. Second, the gap between protein and drug features increases the vulnerability of the obtained classifier thus degrading the prediction robustness. To address these issues, we propose a novel R-DTI method by exploring the second-order relevance in both protein structural feature extraction and DTI prediction phases. Specifically, we construct a pre-trained structural feature extractor that mines the atomic relevance of each amino acid. Then, an inter-feature structure-preserved Riemannian network is designed to expand the existing protein extraction patterns. To improve the prediction robustness, we also develop a Riemannian classifier that uses the second-order protein-drug relevance with a unified feature space. Extensive experimental results demonstrate the merits and superiority of our R-DTI against the state-of-the-art, achieving 1.4% and 1.9% higher AUC-ROC on the BindingDB and DrugBank datasets, respectively.

Yifan Hu, Peiyuan Liu, Peng Zhu, Dawei Cheng, Tao Dai

Transformer-based and MLP-based methods have emerged as leading approaches in time series forecasting (TSF). However, real-world time series often show different patterns at different scales, and future changes are shaped by the interplay of these overlapping scales, requiring high-capacity models. While Transformer-based methods excel in capturing long-range dependencies, they suffer from high computational complexities and tend to overfit. Conversely, MLP-based methods offer computational efficiency and adeptness in modeling temporal dynamics, but they struggle with capturing temporal patterns with complex scales effectively. Based on the observation of multi-scale entanglement effect in time series, we propose a novel MLP-based Adaptive Multi-Scale Decomposition (AMD) framework for TSF. Our framework decomposes time series into distinct temporal patterns at multiple scales, leveraging the Multi-Scale Decomposable Mixing (MDM) block to dissect and aggregate these patterns. Complemented by the Dual Dependency Interaction (DDI) block and the Adaptive Multi-predictor Synthesis (AMS) block, our approach effectively models both temporal and channel dependencies and utilizes autocorrelation to refine multi-scale data integration. Comprehensive experiments demonstrate our AMD framework not only overcomes the limitations of existing methods but also consistently achieves state-of-the-art performance across various datasets.

Xiaoyu Hu, Zhenhua Lin

Posterior drift refers to changes in the relationship between responses and covariates while the distributions of the covariates remain unchanged. In this work, we explore functional linear regression under posterior drift with transfer learning. Specifically, we investigate when and how auxiliary data can be leveraged to improve the estimation accuracy of the slope function in the target model when posterior drift occurs. We employ the approximated least square method together with a lasso penalty to construct an estimator that transfers beneficial knowledge from source data. Theoretical analysis indicates that our method avoids negative transfer under posterior drift, even when the contrast between slope functions is quite large. Specifically, the estimator is shown to perform at least as well as the classical estimator using only target data, and it enhances the learning of the target model when the source and target models are sufficiently similar. Furthermore, to address scenarios where covariate distributions may change, we propose an adaptive algorithm using aggregation techniques. This algorithm is robust against non-informative source samples and effectively prevents negative transfer. Simulation and real data examples are provided to demonstrate the effectiveness of the proposed algorithm.

Xiaolin Hu, Zixuan Gong, Gengze Xu, Wei Liu, Jian Luan, Bin Wang, Yong Liu

Zeroth-order (ZO) optimization as the gradient-free method has become a powerful tool when the first-order gradient is unavailable or expensive to obtain, especially in decentralized learning scenarios where data and computational resources are distributed across multiple clients. There have been many efforts to analyze the optimization convergence rate of zeroth-order decentralized stochastic gradient descent (ZO-DSGD) algorithms. However, the generalization of these methods has not been well studied. In this paper, we provide a generalization analysis of ZO-DSGD with changing topology, where the clients run zeroth-order SGD with local data and communicate with each other according to time-varying topology. We systematically analyze the generalization error in convex, strongly convex, and non-convex cases. The obtained results in the convex and strongly convex cases with zeroth-order oracles recover the results of SGD. Moreover, the generalization bounds derived in non-convex cases align with that of DSGD. To capture the influence of communication topology on the generalization performance, we analyze local generalization bounds concerning local models held at different clients. The obtained results reflect the influence of the number of clients, local sample size, and topology on the generalization error. To the best of our knowledge, this is the first work that provides a generalization analysis of zeroth-order decentralized stochastic gradient descent methods and recovers the results of SGD.

Wen-Chao Hu, Wang-Zhou Dai, Yuan Jiang, Zhi-Hua Zhou

Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with symbolic reasoning. However, for complex learning targets, NeSy systems often generate outputs inconsistent with domain knowledge and it is challenging to rectify them. Inspired by the human Cognitive Reflection, which promptly detects errors in our intuitive response and revises them by invoking the System 2 reasoning, we propose to improve NeSy systems by introducing Abductive Reflection (ABL-Refl) based on the Abductive Learning (ABL) framework. ABL-Refl leverages domain knowledge to abduce a reflection vector during training, which can then flag potential errors in the neural network outputs and invoke abduction to rectify them and generate consistent outputs during inference. ABL-Refl is highly efficient in contrast to previous ABL implementations. Experiments show that ABL-Refl outperforms state-of-the-art NeSy methods, achieving excellent accuracy with fewer training resources and enhanced efficiency.

Sizhe Hu, Wenming Wu, Yuntao Wang, Benzhu Xu, Liping Zheng

Automating architectural floorplan design is vital for housing and interior design, offering a faster, cost-effective alternative to manual sketches by architects. However, existing methods, including rule-based and learning-based approaches, face challenges in design complexity and constrained generation with extensive post-processing, and tend to obvious geometric inconsistencies such as misalignment, overlap, and gaps. In this work, we propose a novel generative framework for vector floorplan design via structural graph generation, called GSDiff, focusing on wall junction generation and wall segment prediction to capture both geometric and semantic aspects of structural graphs. To improve the geometric rationality of generated structural graphs, we propose two innovative geometry enhancement methods. In wall junction generation, we propose a novel alignment loss function to improve geometric consistency. In wall segment prediction, we propose a random self-supervision method to enhance the model’s perception of the overall geometric structure, thereby promoting the generation of reasonable geometric structures. Employing the diffusion model and the Transformer model, as well as the geometry enhancement strategies, our framework can generate wall junctions, wall segments and room polygons with structural and semantic information, resulting in structural graphs that accurately represent floorplans. Extensive experiments show that the proposed method surpasses existing techniques, enabling free generation and constrained generation, marking a shift towards structure generation in architectural design.

Shizhe Hu, Jiahao Fan, Guoliang Zou, Yangdong Ye

Deep multi-modal clustering can extract useful information among modals, thus benefiting the final clustering and many related fields. However, existing multi-modal clustering methods have two major limitations. First, they often ignore different levels of guiding information from both the feature representations and cluster assignments, which thus are difficult in learning discriminative representations. Second, most methods fail to effectively eliminate redundant information between multi-modal data, negatively affecting clustering results. In this paper, we propose a novel multi-aspect self-guided deep information bottleneck (MSDIB) method for multi-modal clustering, which can effectively employ different aspects of guiding information for learning cluster-friendly information among modals. MSDIB mainly contains two parts: information compression and information preservation. In information compression, we extract from the private information of each modality to obtain the compact representation and meanwhile conduct mutual compression between them. In information preservation, the aim is to preserve the shared information among modals and the self-supervised information from the clustering results in each iteration. In the above process, there are mainly three aspects of self-guiding information, the modality-private information, the modality-shared information and the self-supervised pseudo label information. By minimizing the mutual information based objective function with a variational optimization method, we can fully extract useful discriminative information while eliminating the irrelevant parts. Extensive experimental results demonstrate that our method outperforms state-of-the-art multi-modal clustering methods, showcasing its superior performance and broad application prospects.

Shizhe Hu, Binyan Tian, Weibo Liu, Yangdong Ye

Multi-view clustering (MVC), especially contrastive MVC, has demonstrated promising potential in many fields and practical scenarios. However, existing contrastive MVC methods still ignore the reliability of clustering results and the impact of false negative pairs, which limits the application of methods in critical security areas. To solve the above challenges, we propose a Self-supervised Trusted Contrastive Multi-view Clustering with Uncertainty Refined (STCMC-UR) method, which integrates clustering results and uncertainty learning to guide the self-supervised contrastive learning (CL). First, the belief of a specific view is generated in the evidence generation module. Afterwards, the belief mass and uncertainty of each view are learned using the Dirichlet distribution and we fuse multiple views with the Dempster-Shafer theory to generate the final clustering result and the uncertainty of the view. Then, the view weight is further quantified to adjust the belief of each view. Different from existing methods, with the clustering result and uncertainty generated by the fusion, we design a feature-level uncertainty-refined self-supervised CL module, where the pseudo-label is selectively employed in each iteration to conduct more accurate CL. As a result, the modules are mutually beneficial, which is conducive to more effective feature learning and clustering structure discovery, and more accurate learning results are obtained. Extensive experiments on five datasets show that the proposed method has significant improvements in effectiveness compared with the latest methods.

Shengxiang Hu, Guobing Zou, Song Yang, Shiyi Lin, Yanglan Gan, Bofeng Zhang, Yixin Chen

While Large Language Models (LLMs) show promise for Text-Attributed Graphs (TAGs) learning, their deployment is hindered by computational demands. Graph Neural Networks (GNNs) are efficient but struggle with TAGs' complex semantics. We propose LinguGKD, a novel LLM-to-GNN knowledge distillation framework that enables transferring both local semantic details and global structural information from LLMs to GNNs. First, it introduces TAG-oriented instruction tuning, enhancing LLMs with graph-specific knowledge through carefully designed prompts. Next, it develops a layer-adaptive multi-scale contrastive distillation strategy aligning LLM and GNN features at multiple granularities, from node-level to graph-level. Finally, the distilled GNNs combine the semantic richness of LLMs with the computational efficiency of traditional GNNs. Experiments demonstrate that LinguGKD outperforms existing graph distillation frameworks, the distilled simple GNNs achieve comparable or superior performance to more complex GNNs and teacher LLMs, while maintaining computational efficiency. This work bridges the gap between LLMs and GNNs, facilitating advanced graph learning in resource-constrained environments and providing a framework to leverage ongoing LLM advancements for GNN improvement.

Rongyao Hu, Jiangzhang Gan, Mengmeng Zhan, Li Li, Mengling Wei

Unsupervised multi-view feature selection involves selecting a subset of crucial features across diverse views to diminish feature dimensionality without leveraging label information. While numerous studies have delved into this area, current solutions predominantly rely on linear multi-view data or employ weakly supervised learning to aid in feature selection. These approaches may risk losing semantic information when applied to real-world multi-view datasets. In this study, we introduce a novel model, Unsupervised Kernel-based Multi-view Feature selection with Robust self-representation and Binary hashing (UKMFS), which aims to identify robust consistent graph representation across views and leverage binary hashing codes to guide feature selection. Specifically, we first explore the underlying geometry by unifying the dimension of multi-view data with non-linear kernel mapping. Then, we search the consistent graph across views by fusing unique graph representations of each view in a self-representation manner. Additionally, we impose low-rank constraints on the graph of each view to mitigate noise and unimportant parts for preserving the main structures and patterns. Furthermore, we design an unsupervised hashing feature selection model to exploit reliable binary labels across views and weighted matrices from each view. Finally, an effective optimization method is customised to solve the formulated problem iteratively. Comprehensive experiments on public multi-view datasets indicate that our proposed method achieves state-of-the-art performance compared with the representative comparison methods regarding the clustering and the feature selection task.