StructAlign: Structured Cross-Modal Alignment for Continual Text-to-Video Retrieval

• URL: http://arxiv.org/abs/2601.20597v1
• Date: Wed, 28 Jan 2026 13:34:44 GMT
• Title: StructAlign: Structured Cross-Modal Alignment for Continual Text-to-Video Retrieval
• Abstract summary: A key challenge in Continual Text-to-Video Retrieval is feature drift.<n>We propose StructAlign, a structured cross-modal alignment method for CTVR.<n>Our method consistently outperforms state-of-the-art continual retrieval approaches.
• Score: 75.28673512571449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual Text-to-Video Retrieval (CTVR) is a challenging multimodal continual learning setting, where models must incrementally learn new semantic categories while maintaining accurate text-video alignment for previously learned ones, thus making it particularly prone to catastrophic forgetting. A key challenge in CTVR is feature drift, which manifests in two forms: intra-modal feature drift caused by continual learning within each modality, and non-cooperative feature drift across modalities that leads to modality misalignment. To mitigate these issues, we propose StructAlign, a structured cross-modal alignment method for CTVR. First, StructAlign introduces a simplex Equiangular Tight Frame (ETF) geometry as a unified geometric prior to mitigate modality misalignment. Building upon this geometric prior, we design a cross-modal ETF alignment loss that aligns text and video features with category-level ETF prototypes, encouraging the learned representations to form an approximate simplex ETF geometry. In addition, to suppress intra-modal feature drift, we design a Cross-modal Relation Preserving loss, which leverages complementary modalities to preserve cross-modal similarity relations, providing stable relational supervision for feature updates. By jointly addressing non-cooperative feature drift across modalities and intra-modal feature drift, StructAlign effectively alleviates catastrophic forgetting in CTVR. Extensive experiments on benchmark datasets demonstrate that our method consistently outperforms state-of-the-art continual retrieval approaches.

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