Prompt tuning has emerged as an effective rehearsal-free technique for class-incremental learning (CIL) that learns a tiny set of task-specific parameters (or prompts) to instruct a pre-trained transformer to learn on a sequence of tasks. Albeit effective, prompt tuning methods do not lend well in the multi-label class incremental learning (MLCIL) scenario (where an image contains multiple foreground classes) due to the ambiguity in selecting the correct prompt(s) corresponding to different foreground objects belonging to multiple tasks. To circumvent this issue we propose to eliminate the prompt selection mechanism by maintaining task-specific pathways, which allow us to learn representations that do not interact with the ones from the other tasks. Since independent pathways in truly incremental scenarios will result in an explosion of computation due to the quadratically complex multi-head self-attention (MSA) operation in prompt tuning, we propose to reduce the original patch token embeddings into summarized tokens. Prompt tuning is then applied to these fewer summarized tokens to compute the final representation. Our proposed method Multi-Label class incremental learning via summarising pAtch tokeN Embeddings (MULTI-LANE) enables learning disentangled task-specific representations in MLCIL while ensuring fast inference. We conduct experiments in common benchmarks and demonstrate that our MULTI-LANE achieves a new state-of-the-art in MLCIL. Additionally, we show that MULTI-LANE is also competitive in the CIL setting. Source code available at https://github.com/tdemin16/multi-lane

翻译：提示调优已成为一种有效的无排练类增量学习技术，它通过学习一组微小的任务特定参数（即提示）来指导预训练Transformer在任务序列上进行学习。尽管有效，但提示调优方法在多标签类增量学习场景中表现不佳（当图像包含多个前景类别时），因为难以选择与属于多个任务的不同前景对象相对应的正确提示。为解决此问题，我们提出通过维护任务特定路径来消除提示选择机制，这使我们能够学习与其他任务表征互不干扰的表示。由于在真正的增量场景中，独立路径会因提示调优中二次复杂度的多头自注意力操作而导致计算量爆炸式增长，我们提出将原始补丁标记嵌入压缩为总结性标记。随后对这些更少的总结性标记应用提示调优以计算最终表示。我们提出的方法——通过总结补丁标记嵌入实现多标签类增量学习（MULTI-LANE）——能够在MLCIL中学习解耦的任务特定表示，同时确保快速推理。我们在常用基准测试上进行实验，证明MULTI-LANE在MLCIL中达到了新的最优性能。此外，我们还表明MULTI-LANE在CIL设置中同样具有竞争力。源代码位于https://github.com/tdemin16/multi-lane