Anomaly detection is vital in various industrial scenarios, including the
identification of unusual patterns in production lines and the detection of
manufacturing defects for quality control. Existing techniques tend to be
specialized in individual scenarios and lack generalization capacities. In this
study, we aim to develop a generic anomaly detection model applicable across
multiple scenarios. To achieve this, we customize generic visual-language
foundation models that possess extensive knowledge and robust reasoning
abilities into anomaly detectors and reasoners. Specifically, we introduce a
multi-modal prompting strategy that incorporates domain knowledge from experts
as conditions to guide the models. Our approach considers multi-modal prompt
types, including task descriptions, class context, normality rules, and
reference images. In addition, we unify the input representation of
multi-modality into a 2D image format, enabling multi-modal anomaly detection
and reasoning. Our preliminary studies demonstrate that combining visual and
language prompts as conditions for customizing the models enhances anomaly
detection performance. The customized models showcase the ability to detect
anomalies across different data modalities such as images and point clouds.
Qualitative case studies further highlight the anomaly detection and reasoning
capabilities, particularly for multi-object scenes and temporal data. Our code
is available at this https URL

本研究旨在开发一种适用于多个场景的通用异常检测模型，通过将视觉 - 语言基础模型定制为异常检测器和推理器，并引入多模态提示策略，将领域专家的知识作为条件来指导模型，在多模态输入表示中实现多模态异常检测和推理，从而提高异常检测性能。结果表明，该定制模型能够在不同的数据模态（如图像和点云）中检测异常，尤其适用于多物体场景和时间数据。

多模态异常检测与推理的自定义视觉语言基础模型

Customizing Visual-Language Foundation Models for Multi-modal Anomaly  Detection and Reasoning

Accurately predicting the intent of customer support requests is vital for
efficient support systems, enabling agents to quickly understand messages and
prioritize responses accordingly. While different approaches exist for intent
detection, maintaining separate client-specific or industry-specific models can
be costly and impractical as the client base expands.
This work proposes a system to scale intent predictions to various clients
effectively, by combining a single generic model with a per-client list of
relevant intents. Our approach minimizes training and maintenance costs while
providing a personalized experience for clients, allowing for seamless
adaptation to changes in their relevant intents. Furthermore, we propose a
strategy for using the clients relevant intents as model features that proves
to be resilient to changes in the relevant intents of clients -- a common
occurrence in production environments.
The final system exhibits significantly superior performance compared to
industry-specific models, showcasing its flexibility and ability to cater to
diverse client needs.

客户支持请求的准确预测对于高效的支持系统至关重要，该论文提出了一种将意图预测扩展到各个客户的系统，通过将一个通用模型与每个客户的相关意图列表结合使用，以降低培训和维护成本，同时为客户提供个性化体验，以适应其相关意图的变化，并展示了与行业特定模型相比具有显著优势的最终系统，显示出其灵活性和满足不同客户需求的能力。

大规模意图检测：使用相关意图调整通用模型

Intent Detection at Scale: Tuning a Generic Model using Relevant Intents

Neural network quantization methods often involve simulating the quantization
process during training, making the trained model highly dependent on the
target bit-width and precise way quantization is performed. Robust quantization
offers an alternative approach with improved tolerance to different classes of
data-types and quantization policies. It opens up new exciting applications
where the quantization process is not static and can vary to meet different
circumstances and implementations. To address this issue, we propose a method
that provides intrinsic robustness to the model against a broad range of
quantization processes. Our method is motivated by theoretical arguments and
enables us to store a single generic model capable of operating at various
bit-widths and quantization policies. We validate our method's effectiveness on
different ImageNet models.

本文提出了一种基于理论论证的神经网络量化方法，实现了针对多种数据类型和量化策略下模型鲁棒性的内在鲁棒性，能够存储一种通用模型并在不同的位宽和量化策略下进行操作，并在不同的 ImageNet 模型中验证了该方法的有效性。