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[2601.18231] Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction between Feature Alignment and Target Fitting
Summary
This paper presents a framework for optimizing cross-modal fine-tuning by addressing the interaction between feature alignment and target fitting, enhancing model performance across various datasets.
Why It Matters
As the integration of diverse modalities in machine learning becomes crucial, understanding how to effectively align features and targets can significantly improve model generalization and performance. This research provides theoretical insights and practical guidelines for enhancing cross-disciplinary applications.
Key Takeaways
- Introduces a framework for optimizing feature alignment and target fitting.
- Establishes a generalization bound that explains the interaction between features and targets.
- Demonstrates improved performance over state-of-the-art methods on benchmark datasets.
- Highlights the importance of calibrated combinations in model training.
- Offers actionable insights for algorithm design in cross-modal applications.
Computer Science > Machine Learning arXiv:2601.18231 (cs) [Submitted on 26 Jan 2026 (v1), last revised 26 Feb 2026 (this version, v3)] Title:Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction between Feature Alignment and Target Fitting Authors:Trong Khiem Tran, Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang View a PDF of the paper titled Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction between Feature Alignment and Target Fitting, by Trong Khiem Tran and 4 other authors View PDF HTML (experimental) Abstract:Adapting pre-trained models to unseen feature modalities has become increasingly important due to the growing need for cross-disciplinary knowledge integration. A key challenge here is how to align the representation of new modalities with the most relevant parts of the pre-trained model's representation space to enable accurate knowledge transfer. This requires combining feature alignment with target fine-tuning, but uncalibrated combinations can exacerbate misalignment between the source and target feature-label structures and reduce target generalization. Existing work, however, lacks a theoretical understanding of this critical interaction between feature alignment and target fitting. To bridge this gap, we develop a principled framework that establishes a provable generalization bound on the target error, which explains the interaction between feature alignment and target fitting through a novel concept of feature-la...
