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Found 7 papers, 4 papers with code
Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation
In this work, we introduce a Unified Modality Separation (UniMoS) framework for unsupervised domain adaptation.
Agile Multi-Source-Free Domain Adaptation
Efficiently utilizing rich knowledge in pretrained models has become a critical topic in the era of large models.
Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation
Specifically, the image contextual information is utilized to prompt the language branch in a domain-agnostic and instance-conditioned way.
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users demanding access to data from various modalities.
Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer
The primary reason for this situation lies in that the distribution of the external data for validation is different from the distribution of the training data for the construction of the predictive model.
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer
Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced artificial intelligence technologies.
Task-adaptive Asymmetric Deep Cross-modal Hashing
Unlike previous cross-modal hashing approaches, our learning framework jointly optimizes semantic preserving that transforms deep features of multimedia data into binary hash codes, and the semantic regression which directly regresses query modality representation to explicit label.
