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- One-step high-efficiency CRISPR/Cas9-mediated genome editing in Streptomyces291
A high-efficiency CRISPR/Cas9 genome editing plasmid pKCcas9dO for use in Streptomyces genetic manipulation, which comprises a target-specific guide RNA, a codon-optimized cas9, and two homology-directed repair templates has promise for application to genome modification in other Actinomyces species.
- Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis278
A novel framework HyCon is proposed for hybrid contrastive learning of tri-modal representation that outperforms baselines on multimodal sentiment analysis and emotion recognition and can naturally generate many training pairs for better generalization and reduce the negative effect of limited datasets.
- Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion263
A novel adversarial encoder-decoder-classifier framework to learn a modality-invariant embedding space using hierarchical graph neural network which explicitly explores unimodal, bimodal and trimodal interactions in multi-stage fusion.
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- Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition210
A novel model, named Deep Attentive Multi-path Convolutional Neural Network (DAM-CNN), that can automatically locate expression-related regions in an expressional image and yield a robust image representation for FER.
- Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations184
The multi-modal information bottleneck (MIB) is introduced, aiming to learn a powerful and sufficient multimodal representation that is free of redundancy and to filter out noisy information in unimodal representations.
- Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering154
This work introduces a novel self-supervised contrastive learning mechanism to learn the relationship between original samples, factual samples and counterfactual samples and evaluates the effectiveness by surpassing current state-of-the-art models on the VQA-CP dataset.
- Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing149
This work proposes a general strategy named ‘divide, conquer and combine’ for multimodal fusion, which achieves state-of-the-art performance on multi-modalities affective computing with higher efficiency.
- Communicative Message Passing for Inductive Relation Reasoning148
A Communicative Message Passing neural network for Inductive reLation rEasoning, CoMPILE, that reasons over local directed subgraph structures and has a vigorous inductive bias to process entity-independent semantic relations and can naturally handle asymmetric/anti-symmetric relations.
- Hierarchical Attention Network for Image Captioning132
A Hierarchical Attention Network (HAN) is proposed that enables attention to be calculated on pyramidal hierarchy of features synchronously and exploits several multimodal integration strategies, which can significantly improve the performance.
- Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination116
Maximum Entropy Population-based training (MEP) is proposed to mitigate the distributional shift when paired with unencountered partners, such as humans.
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- Innovative Approach for Improvement of an Antibiotic-Overproducing Industrial Strain of Streptomyces albus97
It is concluded that this aberrant protein synthesis ability in the Strr mutant, which is a result of increased stability of the 70S complex, is responsible for the remarkable salinomycin production enhancement obtained.
- Facial expression recognition with FRR‐CNN95
Different from traditional CNN, convolutional kernels of FRR-CNN is induced to be divergent by presenting a more discriminative mutual difference among feature maps of the same layer, which results in generating less redundant features and yields a more compact representation of an image.
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- MissModal: Increasing Robustness to Missing Modality in Multimodal Sentiment Analysis86
A novel representation learning approach named MissModal is proposed, devoting to increasing robustness to missing modality in a classification approach, which achieves superior performance with minimal computational costs in various missing modalities scenarios (flexibility), including severely missing modality (efficiency) on two public MSA datasets.
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- Orthogonal neighborhood preserving discriminant analysis for face recognition85
A new linear subspace analysis algorithm, called orthogonal neighborhood preserving discriminant analysis (ONPDA), for face recognition with consistent and promising results.
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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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