ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation
This paper proposes Efficient Continual Alignment (ECA) for incremental learning in open-ended image-to-text generation. By introducing continual alignment and three core mechanisms (Mixture of Query, Fisher Dynamic Expansion, Dictionary Replay), ECA mitigates catastrophic forgetting without accessing old data, achieving superior performance on new benchmarks.
[2606.12633] ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation
[Submitted on 10 Jun 2026]
Title:ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation
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Abstract:Incremental Learning (IL) for Open-ended Image-to-Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preserving previously acquired knowledge. Unlike prior studies, this paper addresses a more practical scenario in which the predominant category of visual data shifts over time as environments evolve. In this context, we introduce a new notion of continual alignment, which incrementally adapts the alignment module within pre-trained VLMs to preserve high-quality cross-modal representations. Based on this idea, we propose Efficient Continual Alignment (ECA), a novel exemplar-free IL approach for OpenITG. The key challenge is enabling the model to acquire new, task-specific features while minimizing interference with the established alignment without accessing raw data from previous tasks. To address this, ECA employs three core mechanisms: a Mixture of Query (MoQ) module that adapts task-specific query tokens, a Fisher Dynamic Expansion (FeDEx) that dynamically expands model structure based on a Fisher Information Matrix (FIM)-based metric, and an embedding dictionary with Dictionary Replay (DR) to retain past knowledge. To evaluate ECA's performance, we construct four new IL OpenITG benchmarks that better reflect real-world scenarios. Experimental results demonstrate that ECA significantly mitigates catastrophic forgetting and improves IL performance compared to baseline methods. Code and benchmarks are available at this https URL.
Comments: Accepted at the 43rd International Conference on Machine Learning (ICML 2026)
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2606.12633 [cs.CV]
(or arXiv:2606.12633v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.12633
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jiangtao Kong [view email] [v1] Wed, 10 Jun 2026 19:42:03 UTC (3,059 KB)
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