Label propagation (LP) is a popular semi-supervised learning technique that propagates labels from a training dataset to a test one using a similarity graph, assuming that nearby samples should have similar labels. However, the recent cross-domain problem assumes that training (source domain) and test data sets (target domain) follow different distributions, which may unexpectedly degrade the performance of LP due to small similarity weights connecting the two domains. To address this problem, we propose optimal graph learning-based label propagation (OGL2P), which optimizes one cross-domain graph and two intra-domain graphs to connect the two domains and preserve domain-specific structures, respectively. During label propagation, the cross-domain graph draws two labels close if they are nearby in feature space and from different domains, while the intra-domain graph pulls two labels close if they are nearby in feature space and from the same domain. This makes label propagation more insensitive to cross-domain problems. During graph embedding, we optimize the three graphs using features and labels in the embedded subspace to extract locally discriminative and domain-invariant features and make the graph construction process robust to noise in the original feature space. Notably, as a more relaxed constraint, locally discriminative and domain-invariant can somewhat alleviate the contradiction between discriminability and domain-invariance. Finally, we conduct extensive experiments on five cross-domain image classification datasets to verify that OGL2P outperforms some state-of-the-art cross-domain approaches.

Multimodal sentiment analysis has become a popular research topic in recent years. However, existing methods have two unaddressed limitations: (1) they use limited supervised labels to train models, which makes it impossible for model to fully learn sentiments in different modal data; (2) they employ text and image pre-trained models trained in different unimodal tasks to extract different modal features, so that the extracted features cannot take into account the interactive information between image and text. To solve these problems, in this paper we propose a Vision-Language Contrastive Learning network (VLCLNet). First, we introduce a pre-trained Large Language Model (LLM), which is trained from vast quantities of multimodal data, has better understanding ability for image and text contents, thus being effectively applied to different tasks while requiring few amount of labelled training data. Second, we adapt a Multimodal Large Language Model (MLLM), BLIP-2 (Bootstrapping Language-Image Pre-training) network, to extract multimodal fusion feature. Such MLLM can fully consider the correlation between images and texts when extracting features. In addition, due to the discrepancy between the pre-training task and the sentiment analysis task, the pre-trained model will output the suboptimal prediction results. We use Low-Rank Adaptation (LoRA) fine-tuning strategy to update the model parameters on sentiment analysis task, which avoids the issue of inconsistent task between pre-training task and downstream task. Experiments verify that the proposed VLCLNet is superior to other strong baselines.

Catastrophic forgetting has become an intractable problem in the continual learning setting because previous data is not accessible when training. To mitigate this problem, memory-based continual learning methods replay previous data from a fixed-size memory buffer. Reservoir sampling, which can sample uniformly from streaming data in a single pass and randomly delete a few buffered samples to keep the memory capacity, is widely used in the memory update process. Nevertheless, the random deletion will cause the agent to miss some high-quality samples, which are more effective in overcoming forgetting. In this paper, we propose a novel memory update method, Class-level Minimally Interfered Update (CMIU), which can maintain the sampling randomness and remove the worthless samples from the same classes as the randomly deleting samples. Concretely, CMIU deletes the samples that are minimally interfered with the agent update. The experiments show that CMIU presents improved performance compared to the baselines on both balanced and imbalanced data streams in the online continual learning setting.

In this paper, we develop a progressive local and non-local interactive network with multi-scale cross-content deeply discriminative learning to solve image deraining. The proposed model contains two key techniques: 1) Progressive Local and Non-Local Interactive Network (PLNLIN) and 2) Multi-Scale Cross-Content Deeply Discriminative Learning (MCDDL). The PLNLIN is a U-shaped encoder-decoder network, where the proposed new Progressive Local and Non-Local Interactive Module (PLNLIM) is the basic unit in the encoder-decoder framework. The PLNLIM fully explores local and non-local learning in convolution and Transformer operation respectively and the local and non-local content are further interactively learned in a progressive manner. The proposed MCDDL not only discriminates the output of the generator but also receives the deep content from the generator to distinguish real and fake features at each side layer of the discriminator in a multi-scale manner. We show that the proposed MCDDL has fast and stable convergence properties that lack in existing discriminative learning manners. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods on five public synthetic datasets and one real-world data. The source codes will be made available at https://github.com/supersupercong/PLNLIN-MCDDL.

While current CNN-based low-light image enhancement (LIE) approaches have achieved significant progress, they often fail to generate better perceptual quality which requires restoring better details and more natural colors. To address these problems, we set a new path, called PercepLIE, by presenting the VQGAN with Multi-luminance Detail Compensation (MDC) and Global Color Adjustment (GCA). Specifically, observed that latent light features of the low-light images are quite different from those captured in normal light, we utilize VQGAN to explore the latent light representation of normal-light images to help the estimation of the low-light and normal-light mapping. Furthermore, we employ Gamma correction with varying Gamma values on the gradient to create multi-luminance details, forming the basis for our MDC module to facilitate better detail estimation. To optimize the colors of low-light input images, we introduce a simple yet effective GCA module that is based on spatially-varying representation between the estimated normal-light images in this module and low-light inputs. By combining the VQGAN with MDC and GCA within a stage-wise training mechanism, our method generates images with finer details and natural colors and achieves favorable performance on both synthetic and real-world datasets in terms of perceptual quality metrics including NIQE, PI, and LPIPS. The source codes will be made available at https://github.com/supersupercong/PercepLIE.

Multimodal aspect-level sentiment analysis has attracted increasing attention in recent years. However, existing methods have two unaddressed limitations: (1) due to the lack of labelled pre-training data of dedicated sentiment analysis, the methods with a pre-training manner produce suboptimal prediction results; (2) most existing methods employ a self-attention encoder to fuse multimodal tokens, which not only ignores the alignment relationship between different modal tokens but also makes the model unable to capture the semantic links between images and texts. In this paper, we propose a momentum contrastive learning network (MOCOLNet) to overcome above limitations. First, we merge the pre-training stage with the training stage to design an end-to-end training manner which uses less labelled data dedicated to sentiment analysis to obtain better prediction results. Second, we propose a multimodal contrastive learning method to align the different modal representations before data fusing, and design a cross-modal matching strategy to provide semantic interactive information between texts and images. Moreover, we introduce an auxiliary momentum strategy to increase the robustness of model. We also analyse the effectiveness of the proposed multimodal contrastive learning method using a mutual information theory. Experiments verify that the proposed MOCOLNet is superior to other strong baselines.

Advances in single-cell sequencing and data analysis have made it possible to infer biological trajectories spanning heterogeneous cell populations based on transcriptome variation. These trajectories yield a wealth of novel insights into dynamic processes such as development and differentiation. However, trajectory analysis relies on an assumption of trajectory continuity, and experimental limitations preclude some real-world scenarios from meeting this condition. The current lack of assessment metrics makes it difficult to ascertain if/when a given trajectory deviates from continuity, and what impact such a divergence would have on inference accuracy is unclear. By analyzing simulated breaks introduced into in silico and real single-cell data, we found that discontinuity caused precipitous drops in the accuracy of trajectory inference. We then generate a simple scoring algorithm for assessing trajectory continuity, and found that continuity assessments in real-world cases of intestinal stem cell development and CD8 + T cells differentiation efficiently identifies trajectories consistent with empirical knowledge. This assessment approach can also be used in cases where a priori knowledge is lacking to screen a pool of inferred lineages for their adherence to presumed continuity, and serve as a means for weighing higher likelihood trajectories for validation via empirical studies, as exemplified by our case studies in psoriatic arthritis and acute kidney injury. This tool is freely available through github at qingshanni/scEGRET.

Graph clustering, which aims to partition a set of graphs into groups with similar structures, is a fundamental task in data analysis. With the great advances made by deep learning, deep graph clustering methods have achieved success. However, these methods have two limitations: (1) they learn graph embeddings by a neural language model that fails to effectively express graph properties, and (2) they treat embedding learning and clustering as two isolated processes, so the learned embeddings are unsuitable for the subsequent clustering. To overcome these limitations, we propose a novel capsule-based graph clustering (CGC) algorithm to cluster graphs. First, we construct a graph clustering capsule network (GCCN) that introduces capsules to capture graph properties. Second, we design an iterative optimization strategy to alternately update the GCCN parameters and clustering assignment parameters. This strategy leads GCCN to learn cluster-oriented graph embeddings. Experimental results show that our algorithm achieves performance superior to that of existing graph clustering algorithms in terms of three standard evaluation metrics: ACC, NMI, and ARI. Moreover, we use visualization results to analyze the effectiveness of the capsules and demonstrate that GCCN can learn cluster-oriented embeddings.

Existing deep graph clustering methods usually rely on neural language models to learn graph embeddings. However, these methods either ignore node feature information or fail to learn cluster-oriented graph embeddings. In this paper, we propose a novel deep graph clustering framework to tackle these two issues. First, we construct a feature transformation module to effectively integrate node feature information with graph topologies. Second, we introduce a graph embedding module and a self-supervised learning strategy to constrain graph embeddings by leveraging the graph similarity and the self-learning loss to group similar graphs together, thus encouraging the obtained graph embeddings to be cluster-oriented. Extensive experimental results on eight real-world graph datasets validate the superiority of the proposed method over existing ones.

Deep multimodal clustering is challenging, since it needs to learn appropriate features for different modalities and find correct clusters by using consistency among the modalities. Existing methods treat the two problems separately, nevertheless, the optimization of one does not guarantee the optimization of the other. In this paper, we propose an end-to-end Deep Multimodal Clustering (DMMC) framework, which achieves a joint optimization of feature learning and multimodal clustering. We encourage the learned features of each modality to be cluster-oriented by autoencoder with Frobenius norm loss. Then, DMMC integrates features of different modalities by clustering integration layer and learns consensus feature by a consensus loss. The clustering result can be discovered in the consensus feature. Compared with existing clustering methods, our framework achieves superior performances measured by the standard clustering evaluation metrics.

The rapid development of intelligent vehicles brings new challenges to vehicle EMC design, which is benefited from test data-oriented troubleshooting. With the increase in electronic complexity, vehicle on-board system designers should confront with more and more EMC failure possibilities and are in need of effective EMC failure diagnosis approach. However, EMC fault diagnosis is difficult due to the distinguishing features of EMC test dataset, such as small sample, nonlinear, high dimensions, etc. Hence, in this paper, EMC reverse diagnostic model is proposed. Firstly, EMC feature extraction method is designed. Then SVM model is designed to realize EMC faults classification. Corresponding application effect is displayed. Experiment results show that proposed method could match the demand of EMC fault diagnosis for intelligent vehicles.