Set2Seq Transformer: Temporal and Positional-Aware Set Representations for Sequential Multiple-Instance Learning

Sequential multiple-instance learning involves learning representations of sets distributed across discrete timesteps. In many real-world applications, modeling both the internal structure of sets and their temporal relationships across time is essential for capturing complex underlying patterns. However, existing methods either focus on learning set representations at a static level, ignoring temporal dynamics, or treat sequences as ordered lists of individual elements, lacking explicit mechanisms to represent sets. In this work, we propose Set2Seq Transformer, a novel architecture that jointly models permutation-invariant set structure and temporal dependencies by learning temporal and positional-aware representations of sets within a sequence in an end-to-end multimodal manner. We evaluate our Set2Seq Transformer on two tasks that require modeling both set structure alongside temporal and positional patterns, but differ significantly in domain, modality, and objective. First, we consider a fine-art analysis task, modeling artists' oeuvres for predicting artistic success using a novel dataset, WikiArt-Seq2Rank. Second, we utilize our Set2Seq Transformer for a short-term wildfire danger forecasting task. Through extensive experimentation, we show that our Set2Seq Transformer significantly improves over traditional static multiple-instance learning methods by effectively learning permutation-invariant set, temporal, and positional-aware representations across diverse domains, modalities, and tasks. We will release both the dataset and model implementations on GitHub.

@article{efthymiou2025_2408.03404,
  title={ Set2Seq Transformer: Temporal and Positional-Aware Set Representations for Sequential Multiple-Instance Learning },
  author={ Athanasios Efthymiou and Stevan Rudinac and Monika Kackovic and Nachoem Wijnberg and Marcel Worring },
  journal={arXiv preprint arXiv:2408.03404},
  year={ 2025 }
}