Out-of-distribution (OOD) detection is crucial for the safe deployment of
neural networks. Existing CLIP-based approaches perform OOD detection by
devising novel scoring functions or sophisticated fine-tuning methods. In this
work, we propose SeTAR, a novel, training-free OOD detection method that
leverages selective low-rank approximation of weight matrices in
vision-language and vision-only models. SeTAR enhances OOD detection via
post-hoc modification of the model's weight matrices using a simple greedy
search algorithm. Based on SeTAR, we further propose SeTAR+FT, a fine-tuning
extension optimizing model performance for OOD detection tasks. Extensive
evaluations on ImageNet1K and Pascal-VOC benchmarks show SeTAR's superior
performance, reducing the false positive rate by up to 18.95% and 36.80%
compared to zero-shot and fine-tuning baselines. Ablation studies further
validate our approach's effectiveness, robustness, and generalizability across
different model backbones. Our work offers a scalable, efficient solution for
OOD detection, setting a new state-of-the-art in this area.

SeTAR 是一种基于选择性低秩逼近视觉 - 语言和仅视觉模型的训练免费的异常检测方法，通过使用简单的贪婪搜索算法通过后期修改模型的权重矩阵来增强异常检测性能。在 ImageNet1K 和 Pascal-VOC 基准测试上的大量评估结果显示，SeTAR 表现出优越的性能，在将假阳性率降低了多达 18.95% 和 36.80% 的情况下，与零样本和微调基线相比。消融研究进一步验证了我们方法的有效性、鲁棒性和在不同模型背景下的通用性。我们的工作为 OOD 检测提供了一种可扩展、高效的解决方案，树立了这一领域的最新业界标准。

SeTAR：选择性低秩逼近的未知样本检测

SeTAR: Out-of-Distribution Detection with Selective Low-Rank  Approximation

Recently, CLIP-based approaches have exhibited remarkable performance on
generalization and few-shot learning tasks, fueled by the power of contrastive
language-vision pre-training. In particular, prompt tuning has emerged as an
effective strategy to adapt the pre-trained language-vision models to
downstream tasks by employing task-related textual tokens. Motivated by this
progress, in this work we question whether other fundamental problems, such as
weakly supervised semantic segmentation (WSSS), can benefit from prompt tuning.
Our findings reveal two interesting observations that shed light on the impact
of prompt tuning on WSSS. First, modifying only the class token of the text
prompt results in a greater impact on the Class Activation Map (CAM), compared
to arguably more complex strategies that optimize the context. And second, the
class token associated with the image ground truth does not necessarily
correspond to the category that yields the best CAM. Motivated by these
observations, we introduce a novel approach based on a PrOmpt cLass lEarning
(POLE) strategy. Through extensive experiments we demonstrate that our simple,
yet efficient approach achieves SOTA performance in a well-known WSSS
benchmark. These results highlight not only the benefits of language-vision
models in WSSS but also the potential of prompt learning for this problem. The
code is available at this https URL

本文介绍了一种新的 PrOmpt cLass lEarning (POLE) 策略，在弱监督语义分割（WSSS）问题上实现最先进的性能，并强调了语言 - 视觉模型在 WSSS 中的优势和提示学习在该问题中的潜力。