TreeAdapter：用于细粒度物种图像生成的分层分类法引导适配器组合
TreeAdapter: Hierarchical Taxonomy-Guided Adapter Composition for Fine-Grained Species Image Generation
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中文总结 AI 辅助
针对通用文本到图像模型在生成稀有生物物种图像时性能下降的问题，提出TreeAdapter框架，利用分层分类数据，通过在分类树节点附加轻量级适配器及两阶段训练范式，实现准确的细粒度图像生成，性能优于基线。
中文摘要 AI 辅助
尽管通用文本到图像模型在开放域生成中表现出色，但在专门的下游领域，尤其是生成稀有生物物种的图像时，其性能会显著下降。由于长尾分布的影响，通用模型难以捕捉细微的细粒度细节，而针对每个物种的微调方法又过度孤立单个物种，忽略了密切相关分类群之间的共享视觉特征。为了解决这个问题，我们提出了TreeAdapter，这是一个明确利用分层分类数据的新颖框架。TreeAdapter不是使用整体模型或独立的每个物种模块，而是在分类树的每个节点上附加轻量级适配器。具体来说，叶节点适配器捕获特定物种的视觉特征，而内部节点适配器封装后代分类群之间的共享语义。我们引入了一个两阶段训练范式，其中祖先适配器被优化以仅对其后代无法解释的残余视觉特征进行建模。这种模型架构和训练范式使模型能够充分利用分层信息，确保为每个物种准确生成视觉特征。在三个大规模生物多样性基准上进行的广泛实验表明，TreeAdapter实现了最先进的细粒度生成质量，优于通用和特定领域的基线。
英文摘要
Although general text-to-image models excel in open-domain generation, their performance degrades significantly in specialized downstream domains, particularly when generating images of rare biological species. Hindered by long-tailed distributions, general models struggle to capture subtle fine-grained details, while per-species fine-tuning methods over-isolate individual species and consequently ignore the shared visual features among closely related taxa. To address this, we propose TreeAdapter, a novel framework that explicitly leverages hierarchical taxonomic data. Rather than using a monolithic model or independent per-species modules, TreeAdapter attaches lightweight adapters to every node of the taxonomic tree. Specifically, leaf-node adapters capture species-specific visual traits, while internal-node adapters encapsulate shared semantics among descendant taxa. We introduce a two-stage training paradigm where ancestor adapters are optimized to model only the residual visual features unexplained by their descendants. This model architecture and training paradigm enable the model to fully leverage hierarchical information, ensuring the accurate generation of visual features for each species. Extensive experiments across three large-scale biodiversity benchmarks demonstrate that TreeAdapter achieves state-of-the-art fine-grained generation quality, outperforming both general-purpose and domain-specific baselines.
