Heatmaps are widely used to interpret deep neural networks, particularly for
computer vision tasks, and the heatmap-based explainable AI (XAI) techniques
are a well-researched topic. However, most studies concentrate on enhancing the
quality of the generated heatmap or discovering alternate heatmap generation
techniques, and little effort has been devoted to making heatmap-based XAI
automatic, interactive, scalable, and accessible. To address this gap, we
propose a framework that includes two modules: (1) context modelling and (2)
reasoning. We proposed a template-based image captioning approach for context
modelling to create text-based contextual information from the heatmap and
input data. The reasoning module leverages a large language model to provide
explanations in combination with specialised knowledge. Our qualitative
experiments demonstrate the effectiveness of our framework and heatmap
captioning approach. The code for the proposed template-based heatmap
captioning approach will be publicly available.

本论文提出了一个自动、交互式、可扩展且易于访问的框架，包括上下文建模和推理两个模块，提出了一种基于模板的图像标题生成方法，利用大规模语言模型来提供解释，以解决图像热力图的自动化问题。实验证明了该框架和热力图生成方法的有效性。

使用热图字幕和大型语言模型实现深度神经网络自解释性

Towards Self-Explainability of Deep Neural Networks with Heatmap  Captioning and Large-Language Models

Salient object detection is a prevalent computer vision task that has
applications ranging from abnormality detection to abnormality processing.
Context modelling is an important criterion in the domain of saliency
detection. A global context helps in determining the salient object in a given
image by contrasting away other objects in the global view of the scene.
However, the local context features detects the boundaries of the salient
object with higher accuracy in a given region. To incorporate the best of both
worlds, our proposed SaLite model uses both global and local contextual
features. It is an encoder-decoder based architecture in which the encoder uses
a lightweight SqueezeNet and decoder is modelled using convolution layers.
Modern deep based models entitled for saliency detection use a large number of
parameters, which is difficult to deploy on embedded systems. This paper
attempts to solve the above problem using SaLite which is a lighter process for
salient object detection without compromising on performance. Our approach is
extensively evaluated on three publicly available datasets namely DUTS,
MSRA10K, and SOC. Experimental results show that our proposed SaLite has
significant and consistent improvements over the state-of-the-art methods.

本文章提出 SaLite 模型来解决显著目标检测中参数过多难以嵌入式系统的问题，并且该模型采用了全局和局部特征结合的方法，在公开数据集上实现了表现的提升。

SaLite：一种轻量级的显著物体检测模型

SaLite : A light-weight model for salient object detection

This paper proposes an improvement to the existing data-driven Neural Belief
Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT
model uses a hand-crafted belief state update mechanism which involves an
expensive manual retuning step whenever the model is deployed to a new dialogue
domain. We show that this update mechanism can be learned jointly with the
semantic decoding and context modelling parts of the NBT model, eliminating the
last rule-based module from this DST framework. We propose two different
statistical update mechanisms and show that dialogue dynamics can be modelled
with a very small number of additional model parameters. In our DST evaluation
over three languages, we show that this model achieves competitive performance
and provides a robust framework for building resource-light DST models.

本文提出了一种优化现有数据驱动的神经信念跟踪（NBT）对话状态跟踪（DST）框架的方法，该方法使用两个不同的统计更新机制替换手工制作的信仰状态更新机制和基于规则的模块，从而实现非常少的额外模型参数的对话动态建模，从而提供了一种用于构建资源轻量级 DST 模型的强大框架。在三种不同语言的 DST 评估中，我们证明了这种模型的竞争性能。