Cross-modal Unsupervised Domain Adaptation (UDA) becomes a research hotspot because it reduces the laborious annotation of target domain samples. Existing methods only mutually mimic the outputs of cross-modality in each domain, which enforces the class probability distribution agreeable in different domains. However, these methods ignore the complementarity brought by the modality fusion representation in cross-modal learning. In this paper, we propose a cross-modal UDA method for 3D semantic segmentation via Bidirectional Fusion-then-Distillation, named BFtD-xMUDA, which explores cross-modal fusion in UDA and realizes distribution consistency between outputs of two domains not only for 2D image and 3D point cloud but also for 2D/3D and fusion. Our method contains three significant components: Model-agnostic Feature Fusion Module (MFFM), Bidirectional Distillation (B-Distill), and Cross-modal Debiased Pseudo-Labeling (xDPL). MFFM is employed to generate cross-modal fusion features for establishing a latent space, which enforces maximum correlation and complementarity between two heterogeneous modalities. B-Distill is introduced to exploit bidirectional knowledge distillation which includes cross-modality and cross-domain fusion distillation, and well-achieving domain-modality alignment. xDPL is designed to model the uncertainty of pseudo-labels by self-training scheme. Extensive experimental results demonstrate that our method outperforms state-of-the-art competitors in several adaptation scenarios.
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Graph Neural Networks (GNNs) have been the standard toolkit for processing non-euclidean spatial data since their powerful capability in graph representation learning. Unfortunately, their training strategy for network parameters is inefficient since it is directly inherited from classic Neural Networks (NNs), ignoring the characteristic of GNNs. To alleviate this issue, experimental analyses are performed to investigate the knowledge captured in classifier parameters during network training. We conclude that the parameter features, i.e., the column vectors of the classifier parameter matrix, are cluster representations with high discriminability. And after a theoretical analysis, we conclude that the discriminability of these features is obtained from the feature propagation from nodes to parameters. Furthermore, an experiment verifies that compared with cluster centroids, the parameter features are more potential for augmenting the feature propagation between nodes. Accordingly, a novel GNN-specific training framework is proposed by simultaneously updating node representations and classifier parameters via a unified feature propagation scheme. Moreover, two augmentation schemes are implemented for the framework, named Full Propagation Augmentation (FPA) and Simplified Full Propagation Augmentation (SFPA). Specifically, FPA augmentates the feature propagation of each node with the updated classifier parameters. SFPA only augments nodes with the classifier parameters corresponding to their clusters. Theoretically, FPA is equivalent to optimizing a novel graph learning objective, which demonstrates the universality of the proposed framework to existing GNNs. Extensive experiments demonstrate the superior performance and the universality of the proposed framework.
Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view~(FOV) scene. The primary challenge of this task is to explore the relations between multi-spectral images for aligning and integrating multi-view scenes. Capitalizing on the strengths of Graph Convolutional Networks (GCNs) in modeling feature relationships, we propose a spatial graph reasoning based multi-spectral image stitching method that effectively distills the deformation and integration of multi-spectral images across different viewpoints. To accomplish this, we embed multi-scale complementary features from the same view position into a set of nodes. The correspondence across different views is learned through powerful dense feature embeddings, where both inter- and intra-correlations are developed to exploit cross-view matching and enhance inner feature disparity. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features, generating informative and reliable wide FOV scenes. Moreover, we release a challenging dataset named ChaMS, comprising both real-world and synthetic sets with significant parallax, providing a new option for comprehensive evaluation. Extensive experiments demonstrate that our method surpasses the state-of-the-arts.
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels
PDF ↗Since annotating medical images for segmentation tasks commonly incurs expensive costs, it is highly desirable to design an annotation-efficient method to alleviate the annotation burden. Recently, contrastive learning has exhibited a great potential in learning robust representations to boost downstream tasks with limited labels. In medical imaging scenarios, ready-made meta labels (i.e., specific attribute information of medical images) inherently reveal semantic relationships among images, which have been used to define positive pairs in previous work. However, the multi-perspective semantics revealed by various meta labels are usually incompatible and can incur intractable "semantic contradiction" when combining different meta labels. In this paper, we tackle the issue of "semantic contradiction" in a gradient-guided manner using our proposed Gradient Mitigator method, which systematically unifies multi-perspective meta labels to enable a pre-trained model to attain a better high-level semantic recognition ability. Moreover, we emphasize that the fine-grained discrimination ability is vital for segmentation-oriented pre-training, and develop a novel method called Gradient Filter to dynamically screen pixel pairs with the most discriminating power based on the magnitude of gradients. Comprehensive experiments on four medical image segmentation datasets verify that our new method GCL: (1) learns informative image representations and considerably boosts segmentation performance with limited labels, and (2) shows promising generalizability on out-of-distribution datasets.
Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and consistent information among multiple graphs. However, previous methods usually overlook the issues in practical applications, i.e., the out-of-sample issue and the noise issue. To address the above issues, in this paper, we propose an effective and efficient UMGL method to explore both complementary and consistent information. To do this, our method employs multiple MLP encoders rather than graph convolutional network (GCN) to conduct representation learning with two constraints, i.e., preserving the local graph structure among nodes to handle the out-of-sample issue, and maximizing the correlation of multiple node representations to handle the noise issue. Comprehensive experiments demonstrate that our proposed method achieves superior effectiveness and efficiency over the comparison methods and effectively tackles those two issues. Code is available at https://github.com/LarryUESTC/CoCoMG.
Acoustic waves are forms of energy that propagate through various mediums. They can be represented by different modalities, such as auditory signals and visual patterns. The two modalities are often described as one-dimensional waveform in the time domain and two-dimensional spectrogram in the frequency domain. Most acoustic signal processing methods use single modal data for input and training models. This poses a challenge for black-box adversarial attacks on audio signals because the input modality is also unknown to the attacker. In fact, there currently exist no methods that explore the cross-modal transferability of adversarial perturbation. This paper investigates the cross-modal transferability from waveform to spectrogram. We argue that the data distributions in the sample space with the different modalities have mapping relations and propose a novel decision-based cross-modal and cross-medium adversarial attack method. Specifically, it generates an initial example with cross-modal attack capability by combining random natural noise, then iteratively reduces the perturbation to enhance its invisibility. It incorporates the constraints of the spectrogram sample space while iteratively optimizing adversarial perturbations for black-box audio classification models. The perturbation is imperceptible to humans, both visually and aurally. Extensive experiments demonstrate that our approach can launch attacks on classification models for sound waves and spectrograms that share the same audio signal. Furthermore, we explore the cross-medium capability of our proposed adversarial attack strategy that can target processing models for acoustic signals propagating in air and seawater. The proposed method has preeminent invisibility and generalization compared to other methods.
Contrastive learning on graphs aims at extracting distinguishable high-level representations of nodes. We theoretically illustrate that the entropy of a dataset is approximated by maximizing the lower bound of the mutual information across different views of a graph, i.e., entropy is estimated by a neural network. Based on this finding, we propose a simple yet effective subset sampling strategy to contrast pairwise representations between views of a dataset. In particular, we randomly sample nodes and edges from a given graph to build the input subset for a view. Two views are fed into a parameter-shared Siamese network to extract the high-dimensional embeddings and estimate the information entropy of the entire graph. For the learning process, we propose to optimize the network using two objectives, simultaneously. Concretely, the input of the contrastive loss consists of positive and negative pairs. Our selection strategy of pairs is different from previous works and we present a novel strategy to enhance the representation ability by selecting nodes based on cross-view similarities. We enrich the diversity of the positive and negative pairs by selecting highly similar samples and totally different data with the guidance of cross-view similarity scores, respectively. We also introduce a cross-view consistency constraint on the representations generated from the different views. We conduct experiments on seven graph benchmarks, and the proposed approach achieves competitive performance compared to the current state-of-the-art methods. The source code is available at https://github.com/kunzhan/M-ILBO.
Effective alignment and fusion of multimodal features remain a significant challenge for multimodal sentiment analysis. In various multimodal applications, the text modal exhibits a significant advantage of compact yet expressive representation ability. In this paper, we propose a Cross-modality Representation Interactive Learning (CRIL) approach, which adopts the text modality to guide other modalities for learning representative feature tokens, contributing to effective multimodal fusion in multimodal sentiment analysis. We propose a semantic representation interactive learning module to learn concise semantic representation tokens for audio and video modalities under the guidance of the text modality, ensuring semantic alignment of representations among multiple modalities. Furthermore, we design a semantic relationship interactive learning module, which calculates a self-attention matrix for each modality and controls their consistency to enable the semantic relationship alignment for multiple modalities. Finally, we present a two-stage interactive fusion solution to bridge the modality gap for multimodal fusion and sentiment analysis. Extensive experiments are performed on the CMU-MOSEI, CMU-MOSI, and UR-FUNNY datasets, and experiment results demonstrate the effectiveness of our proposed approach.
Advanced deep multi-view subspace clustering methods are based on the self-expressive model, which has achieved impressive performance. However, most existing works have several limitations: 1) They endure high computational complexity when learning a consistent affinity matrix, impeding their capacity to handle large-scale multi-view data; 2) The global and local structure information of multi-view data remains under-explored. To tackle these challenges, we propose a simplistic but comprehensive framework called Multi-view Self-Expressive Subspace Clustering (MSESC) network. Specifically, we design a deep metric network to replace the conventional self-expressive model, which can directly and efficiently produce the intrinsic similarity values of any instance-pairs of all views. Moreover, our method explores global and local structure information from the connectivity of instance-pairs across views and the nearest neighbors of instance-pairs within the view, respectively. By integrating global and local structure information within a unified framework, MSESC can learn a high-quality shared affinity matrix for better clustering performance. Extensive experimental results indicate the superiority of MSESC compared to several state-of-the-art methods.
By integrating complementary information from RGB image and depth map, the ability of salient object detection (SOD) for complex and challenging scenes can be improved. In recent years, the important role of Convolutional Neural Networks (CNNs) in feature extraction and cross-modality interaction has been fully explored, but it is still insufficient in modeling global long-range dependencies of self-modality and cross-modality. To this end, we introduce CNNs-assisted Transformer architecture and propose a novel RGB-D SOD network with Point-aware Interaction and CNN-induced Refinement (PICR-Net). On the one hand, considering the prior correlation between RGB modality and depth modality, an attention-triggered cross-modality point-aware interaction (CmPI) module is designed to explore the feature interaction of different modalities with positional constraints. On the other hand, in order to alleviate the block effect and detail destruction problems brought by the Transformer naturally, we design a CNN-induced refinement (CNNR) unit for content refinement and supplementation. Extensive experiments on five RGB-D SOD datasets show that the proposed network achieves competitive results in both quantitative and qualitative comparisons. Our code is publicly available at: https://github.com/rmcong/PICR-Net_ACMMM23.
Layout planning is centrally important in the field of architecture and urban design. Among the various basic units carrying urban functions, residential community plays a vital part for supporting human life. Therefore, the layout planning of residential community has always been of concern, and has attracted particular attention since the advent of deep learning that facilitates the automated layout generation and spatial pattern recognition. However, the research circles generally suffer from the insufficiency of residential community layout benchmark or high-quality datasets, which hampers the future exploration of data-driven methods for residential community layout planning. The lack of datasets is largely due to the difficulties of large-scale real-world residential data acquisition and long-term expert screening. In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date. ReCo Dataset is presented in multiple data formats with 37,646 residential community layout plans, covering 598,728 residential buildings with height information. ReCo can be conveniently adapted for residential community layout related urban design tasks, e.g., generative layout design, morphological pattern recognition and spatial evaluation. To validate the utility of ReCo in automated residential community layout planning, two Generative Adversarial Network (GAN) based generative models are further applied to the dataset. We expect ReCo Dataset to inspire more creative and practical work in intelligent design and beyond. The ReCo Dataset is published at: https://www.kaggle.com/fdudsde/reco-dataset and related code can be found at: \urlhttps://github.com/FDUDSDE/ReCo-Dataset.
The pretrain-then-finetune paradigm has been widely used in various unimodal and multimodal tasks. However, finetuning all the parameters of a pre-trained model becomes prohibitive as the model size grows exponentially. To address this issue, the adapter mechanism that freezes the pre-trained model and only finetunes a few extra parameters is introduced and delivers promising results. Most studies on adapter architectures are dedicated to unimodal or bimodal tasks, while the adapter architectures for trimodal tasks have not been investigated yet. This paper introduces a novel Long Short-Term Trimodal Adapter (LSTTA) approach for video understanding tasks involving audio, visual, and language modalities. Based on the pre-trained from the three modalities, the designed adapter module is inserted between the sequential blocks to model the dense interactions across the three modalities. Specifically, LSTTA consists of two types of complementary adapter modules, namely the long-term semantic filtering module and the short-term semantic interaction module. The long-term semantic filtering aims to characterize the temporal importance of the video frames and the short-term semantic interaction module models local interactions within short periods. Compared to previous state-of-the-art trimodal learning methods pre-trained on a large-scale trimodal corpus, LSTTA is more flexible and can inherit any powerful unimodal or bimodal models. Experimental results on four typical trimodal learning tasks show the effectiveness of LSTTA over existing state-of-the-art methods.
Existing methods for video question answering (VideoQA) often suffer from spurious correlations between different modalities, leading to a failure in identifying the dominant visual evidence and the intended question. Moreover, these methods function as black boxes, making it difficult to interpret the visual scene during the QA process. In this paper, to discover critical video segments and frames that serve as the visual causal scene for generating reliable answers, we present a causal analysis of VideoQA and propose a framework for cross-modal causal relational reasoning, named Visual Causal Scene Refinement (VCSR). Particularly, a set of causal front-door intervention operations is introduced to explicitly find the visual causal scenes at both segment and frame levels. Our VCSR involves two essential modules: i) the Question-Guided Refiner (QGR) module, which refines consecutive video frames guided by the question semantics to obtain more representative segment features for causal front-door intervention; ii) the Causal Scene Separator (CSS) module, which discovers a collection of visual causal and non-causal scenes based on the visual-linguistic causal relevance and estimates the causal effect of the scene-separating intervention in a contrastive learning manner. Extensive experiments on the NExT-QA, Causal-VidQA, and MSRVTT-QA datasets demonstrate the superiority of our VCSR in discovering visual causal scene and achieving robust video question answering.
In practical scenarios, partial missing of multi-view data is very common, such as register information missing from social network analysis, which results in incomplete multi-view clustering (IMVC). How to fill missing data fast and efficiently plays a vital role in improving IMVC, carrying a significant challenge. Existing IMVC methods always use all observed data to fill in missing data, resulting in high complexity and poor imputation quality due to a lack of guidance from consistent distribution. To break the existing limitations, we propose a novel Distribution Consistency based Fast Anchor Imputation for Incomplete Multi-view Clustering (DCFAI-IMVC) method. Specifically, to eliminate the interference of redundant and fraudulent features in the original space, incomplete data are first projected into a consensus latent space, where we dynamically learn a small number of anchors to achieve fast and good imputation. Then, we employ global distribution information of the observed embedding representations to further ensure the consistent distribution between the learned anchors and the observed embedding representations. Ultimately, a tensor low-rank constraint is imposed on bipartite graphs to investigate the high-order correlations hidden in data. DCFAI-IMVC enjoys linear complexity in terms of sample number, which gives it great potential to handle large-scale IMVC tasks. By performing extensive experiments, our effectiveness, superiority, and efficiency are all validated on multiple public datasets with recent advances.
Pan-sharpening aims to utilize the high-resolution panchromatic (PAN) image as a guidance to super-resolve the spatial resolution of the low-resolution multispectral (MS) image. The key challenge in pan-sharpening is how to effectively and precisely inject high-frequency edges and textures from the PAN image into the low-resolution MS image. To address this issue, we propose a High-frequency Feature Enhancement and Alignment Network (HFEAN) for effectively encouraging the high-frequency learning. To implement it, three core designs are customized: a Fourier convolution based efficient feature enhancement module (FEM), an implicit neural alignment module (INA), and a preliminary alignment module (Pre-align). To be specific, FEM employs the fast Fourier convolution with attention mechanism to achieve the mixed global-local receptive field on each scale of the high-frequency domain, thus yielding the informative latent codes. INA leverages implicit neural function to precisely align the latent codes from different scales in the continuous domain. In this way, the high frequency signals at different scales are represented as functions of continuous coordinates, enabling a precise feature alignment in a resolution-free manner. Pre-align is developed to further address the inherent misalignment between PAN and MS pairs. Extensive experiments over multiple satellite datasets validate the effectiveness of the proposed network and demonstrate its favorable performance against the existing state-of-the-art methods both visually and quantitatively. Code is available at: https://github.com/Gracewangyy/HFEAN.
Pansharpening is a challenging low-level vision task whose aim is to learn the complementary representation between spectral information and spatial detail. Despite the remarkable progress, existing deep neural network (DNN) based pansharpening algorithms are still confronted with common limitations. 1) These methods rarely consider the local specificity of different spectral bands; 2) They often extract the global detail in the spatial domain, which ignore the task-related degradation, e.g., the down-sampling process of MS image, and also suffer from limited receptive field. In this work, we propose a novel bidomain modeling paradigm for pansharpening problem (dubbed as BiMPan), which takes into both local spectral specificity and global spatial detail. More specifically, we first customize the specialized source-discriminative adaptive convolution (SDAConv) for every spectral band instead of sharing the identical kernels across all bands like prior works. Then, we devise a novel Fourier global modeling module (FGMM), which is capable of embracing global information while benefiting the disentanglement of image degradation. By integrating the band-aware local feature and Fourier global detail from these two functional designs, we can fuse a texture-rich while visually pleasing high-resolution MS image. Extensive experiments demonstrate that the proposed framework achieves favorable performance against current state-of-the-art pansharpening methods. The code is available at https://github.com/coder-qicao/BiMPan.
Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the following drawback, which limits the clustering performance from further improvement. The existing multi-view models mainly focus on the consistency of the same samples in different views while ignoring the circumstance of similar but different samples in cross-view scenarios. To solve this problem, we propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC). Specifically, we first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. Moreover, to utilize the diversity of multi-view information, we propose a local contrastive calibration loss to constrain the consistency of pair-wise view features. The feature structure is regularized by reliable class information, thus guaranteeing similar samples have similar features in different views. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels. In comparison with other state-of-the-art approaches, the comprehensive experimental results obtained from eight benchmark datasets provide substantial validation of the effectiveness and superiority of our algorithm. We release the code of DealMVC at https://github.com/xihongyang1999/DealMVC on GitHub.
High-order Complementarity Induced Fast Multi-View Clustering with Enhanced Tensor Rank Minimization
Recently, tensor-based multi-view clustering methods have achieved promising results, primarily benefited from their superior ability in exploring high-order consistent information among views. Despite significant progress, these methods inevitably suffer from several drawbacks: 1) Extremely high computational complexity restricts their feasibility for large-scale data sets. 2) Prevalently adopted tensor rank approximations (e.g., Tensor Nuclear Norm (TNN)) tend to under-penalize small singular values, resulting in noise residuals. 3) Tensor structure is rarely utilized for high-order complementarity investigation. In light of this, we propose High-order Complementarity Induced Fast Multi-View Clustering with Enhanced Tensor Rank Minimization (CFMVC-ETR). Specifically, two sets of representation matrices are learned from original multi-view data via the matrix factorization mechanism with a group of base matrices, which are further reconstructed into the consistent tensor and the complementary tensor, respectively. Subsequently, a novel Enhanced Tensor Rank is imposed on the consistent tensor, which is a tighter approximation of the tensor rank and is more noisy-robust to explore the high-order consistency. Meanwhile, a tensor-level constraint termed Tensorial Exclusive Regularization is proposed on the complementary tensor to enhance the view-specific feature and well capture the high-order complementarity. Moreover, we adopt a concatenation-fusion approach to integrate these two parts, deriving a discriminative unified embedding for the clustering task. We solve CFMVC-ETR by an efficient algorithm with good convergence. Extensive experiments on nine challenging data sets demonstrate the superiority of the proposed method.
Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised graph learning. The existing methods learn the sampling distribution of a pre-defined augmentation to generate data-driven augmentations automatically. Although promising clustering performance has been achieved, we observe that these strategies still rely on pre-defined augmentations, the semantics of the augmented graph can easily drift. The reliability of the augmented view semantics for contrastive learning can not be guaranteed, thus limiting the model performance. To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (COVERT). Specifically, in our method, the data augmentations are processed by the proposed reversible perturb-recover network. It distills reliable semantic information by recovering the perturbed latent embeddings. Moreover, to further guarantee the reliability of semantics, a novel semantic loss is presented to constrain the network via quantifying the perturbation and recovery. Lastly, a label-matching mechanism is designed to guide the model by clustering information through aligning the semantic labels and the selected high-confidence clustering pseudo labels. Extensive experimental results on seven datasets demonstrate the effectiveness of the proposed method. We release the code and appendix of CONVERT at https://github.com/xihongyang1999/CONVERT on GitHub.
Graph neural networks (GNNs) have achieved impressive impressions for graph-related tasks. However, most GNNs are primarily studied under the cases of signal domain with supervised training, which requires abundant task-specific labels and is difficult to transfer to other domains. There are few works focused on domain adaptation for graph node classification. They mainly focused on aligning the feature space of the source and target domains, without considering the feature alignment between different categories, which may lead to confusion of classification in the target domain. However, due to the scarcity of labels of the target domain, we cannot directly perform effective alignment of categories from different domains, which makes the problem more challenging. In this paper, we present the Spectral Augmentation for Graph Domain Adaptation (SA-GDA) for graph node classification. First, we observe that nodes with the same category in different domains exhibit similar characteristics in the spectral domain, while different classes are quite different. Following the observation, we align the category feature space of different domains in the spectral domain instead of aligning the whole features space, and we theoretical proof the stability of proposed SA-GDA. Then, we develop a dual graph convolutional network to jointly exploits local and global consistency for feature aggregation. Last, we utilize a domain classifier with an adversarial learning submodule to facilitate knowledge transfer between different domain graphs. Experimental results on a variety of publicly available datasets reveal the effectiveness of our SA-GDA.