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

ArXi:2408.03404v3 Announce Type: replace-cross In many real-world applications, modeling both the internal structure of sets and their temporal relationships is essential for capturing complex underlying patterns. Sequential multiple-instance learning aims to address this challenge by learning permutation-invariant representations of sets distributed across discrete timesteps.