LSA-PAST: Layer-Specific Adaptation and Prototype Transport for Compositional Incremental Learning
Class-Incremental Learning (CIL) enables models to recognize new classes over time while retaining prior knowledge, but many real-world problems are compositional, where visual concepts arise from combinations of primitives such as states (i.e., red, shiny) and objects (i.e., shoe, car).
ProofPaper ↗
Key points
- Compositional Incremental Learning (composition-IL) formalizes this setting by introducing new state-object compositions across tasks.
- In this paper, we propose Layer-Specific Adaptation with Prototype-Anchored Adapter-Space Transport (LSA-PAST), a parameter-efficient method tailored to composition-IL.
- LSA integrates Low-Rank Adaptation (LoRA) into a frozen ViT with depth-specialized adapters for states, objects, and compositions.
- Extensive experiments demonstrate that LSA-PAST achieves state-of-the-art (SOA) accuracy-forgetting trade-off performance for composition-IL.
Sources (1)
- [1]LSA-PAST: Layer-Specific Adaptation and Prototype Transport for Compositional Incremental LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:37 PM
Class-Incremental Learning (CIL) enables models to recognize new classes over time while retaining prior knowledge, but many real-world problems are compositional, where visual concepts arise from combinations of primitives such as states (i.e., red, shiny) and objects (i.e., shoe, car).
Compositional Incremental Learning (composition-IL) formalizes this setting by introducing new state-object compositions across tasks.
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