Likelihood-Aware Semantic Alignment for Full-Spectrum
  Out-of-Distribution Detection
Fan Lu Kai Zhu Kecheng Zheng Wei Zhai Yang Cao
Full-spectrum out-of-distribution (F-OOD) detection aims to accurately
recognize in-distribution (ID) samples while encountering semantic and
covariate shifts simultaneously. However, existing out-of-distribution (OOD)
detectors tend to overfit the covariance information and ignore intrinsic
semantic correlation, inadequate for adapting to complex domain
transformations. To address this issue, we propose a Likelihood-Aware Semantic
Alignment (LSA) framework to promote the image-text correspondence into
semantically high-likelihood regions. LSA consists of an offline Gaussian
sampling strategy which efficiently samples semantic-relevant visual embeddings
from the class-conditional Gaussian distribution, and a bidirectional prompt
customization mechanism that adjusts both ID-related and negative context for
discriminative ID/OOD boundary. Extensive experiments demonstrate the
remarkable OOD detection performance of our proposed LSA especially on the
intractable Near-OOD setting, surpassing existing methods by a margin of
$15.26\%$ and $18.88\%$ on two F-OOD benchmarks, respectively.