UniVAD v2 improves 1N-shot mean image-level AUC from 83.0% to 84.5% (85.7% with one abnormal reference) via support-conditioned boundary construction on six datasets.
Transformers can learn temporal difference methods for in-context reinforce- ment learning,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.
citing papers explorer
-
UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction
UniVAD v2 improves 1N-shot mean image-level AUC from 83.0% to 84.5% (85.7% with one abnormal reference) via support-conditioned boundary construction on six datasets.
-
One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning
Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.