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- Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis678
This work proposes a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs (inter-modality) and between multimodal fusion result and unimmodal input in order to maintain task-related information through multimodAL fusion.
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- A watermark strategy for quantum images based on quantum fourier transform169
A robust watermark strategy for quantum images that embeds the watermark image into the fourier coefficients of the quantum carrier image, which will not affect the carrier image’s visual effect.
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- Bayesian Federated Learning: A Survey58
A critical overview of BFL is presented, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of B FL from both Bayesian and federated perspectives.
- Geometry-Contrastive Generative Adversarial Network for Facial Expression Synthesis36
A geometry-contrastive generative adversarial network GC-GAN for generating facial expression images conditioned on geometry information so that an identity-preserving face can be generated guided by the target expression.
- Single-image dehazing via depth-guided deep retinex decomposition25
A depth-guided retinex decomposition network, which consists of a boosted decoder with a fusion attention mechanism to optimize the illumination iteratively, giving rise to a refined reflectance, that could effectively realize dehazing in nighttime.
- A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms25
This model achieves the highest performance in comparison with several recently-proposed methods and enables to build up the essential interaction between the subdividing detection branch and segmentation branch to fully make use of their correlation.
- Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation23
Motivated by the success of group-wise convolution, grouped connections across the features extracted by fully connected layers are adopted to construct tunable parts in a consolidator to enhance the model's capacity to transfer knowledge under a constrained storage budget and keep inference efficient.
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- Automated diatom searching in the digital scanning electron microscopy images of drowning cases using the deep neural networks22
A deep learning-based approach for automatically searching diatoms in scanning electron microscopic images can satisfy the necessary requirements to be integrated as part of an automatic forensic diatom test.
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- Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation19
The proposed C-VisDiT consists of a cross-domain visual dispersal strategy that transfers only high-confidence source knowledge for model adaptation and an intra-domain visual dispersal strategy that guides the learning of hard target samples with easy ones that significantly outperforms state-of- the-art FUDA methods.
- SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning19
This paper proposes SANCL: Selective Attention and Natural Contrastive Learning for MRHP, which adopts a probe-based strategy to enforce high attention weights on the regions of greater significance and constructs a contrastive learning framework based on natural matching properties in the dataset.
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- An Anisotropic Gaussian Filtering Model for Image De-Hazing17
A de-hazing algorithm on the basis of the anisotropic Gaussian filtering method to overcome some essential limitations of the DCP-based (the dark channel prior, DCP) methods, such as halo artifacts and over-saturation problems is proposed.
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- Convergence Analysis for the SMC‐MeMBer and SMC‐CBMeMBer Filters17
This paper proves that the SMC-MeMBer andSMC-CBMe MBer filters, respectively, converge to the true Me MBer and CB meMBer filters in the mean-square sense and the corresponding bounds for the mean ofsquare errors are given.
- Entropy-based risk network identification in adolescent self-injurious behavior using machine learning and network analysis16
Key risk factors for SIB are identified, including loneliness, ADHD symptoms, Internet addiction, anxiety, depression, affinity for solitude, autistic traits, being bullied, and the integration of EWM, network analysis, and machine learning provides a more precise risk assessment approach.
- Low-resource extraction with knowledge-aware pairwise prototype learning16
K-HPN is able to recognize inherent correlation among classes, where each class is represented as a prototype on the hypersphere and outperforms previous methods of KE, particularly with low-resource training data regimes.
- Learning to Infer Unseen Single-/ Multi-Attribute-Object Compositions With Graph Networks13
An attribute-object semantic association graph model is proposed to learn the complex relations and enable knowledge transfer between primitives and two novel metrics Hard and Soft are proposed to give a comprehensive evaluation in the multi-attribute setting.
- Transformer-Based Approach Via Contrastive Learning for Zero-Shot Detection13
The Trans-ZSD framework is proposed, which is a transformer-based multi-scale contextual detection framework that explicitly exploits inter-class correlations between seen and unseen classes and optimizes feature distribution to learn discriminative features.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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