DVLA-RL++: Dual-Level Vision-Language Alignment with Reinforcement Learning Gating for Few-Shot Learning
To address this problem, we propose DVLA-RL++, which extends DVLA-RL with complementary semantic purification (CSP) and counterfactual reinforcement-learning gating (CRG).
ProofPaper ↗
Key points
- Few-shot learning aims to recognize novel categories from limited labeled examples.
- Recent studies incorporate textual semantics to compensate for limited visual observations and improve class representations.
- An ambiguity-dependent rejection margin guides sparse evidence allocation, while an intrinsic semantic anchor fills the unassigned mass to provide a fallback when visual evidence is unreliable.
- CRG learns layer-wise semantic fusion strengths using a reward that balances recognition performance and nuisance exposure.
Sources (1)
- [1]DVLA-RL++: Dual-Level Vision-Language Alignment with Reinforcement Learning Gating for Few-Shot LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:01 PM
To address this problem, we propose DVLA-RL++, which extends DVLA-RL with complementary semantic purification (CSP) and counterfactual reinforcement-learning gating (CRG).
Few-shot learning aims to recognize novel categories from limited labeled examples.
Extractive summary: sentences quoted from the sources.