<ul><li>Sequential multiple-instance learning involves learning representations of sets distributed across discrete timesteps.</li><li>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.</li><li>Set2Seq Transformer is 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.</li><li>The Set2Seq Transformer significantly improves over traditional static multiple-instance learning methods by effectively learning permutation-invariant set, temporal, and positional-aware representations.</li></ul>

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

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