通过t-FCW图表示实现统一的非参数化且可解释的点云分析

图记忆：一种结构化且可解释的多模态嵌入推理框架

Graph Memory: A Structured and Interpretable Framework for Modality-Agnostic Embedding-Based Inference

图记忆通过紧凑的可靠性标注原型区域图表示嵌入空间，结合原型关系编码局部几何和区域模糊性，实现查询证据扩散推理，统一实例检索、原型推理和图扩散，提供更准确和可解释的多模态推理。

Interpretable Visualizations with Differentiating Embedding Networks

We present a visualization algorithm based on a novel unsupervised Siamese neural network training regime and loss function, called Differentiating Embedding Networks (DEN). The Siamese neural network finds differentiating or similar features between specific pairs of samples in a dataset, and uses these features to embed the dataset in a lower dimensional space where it can be visualized. Unlike existing visualization algorithms such as UMAP or $t$-SNE, DEN is parametric, meaning it can be interpreted by techniques such as SHAP. To interpret DEN, we create an end-to-end parametric clustering …

BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of Attention

The prevalence of employing attention mechanisms has brought along concerns on the interpretability of attention distributions. Although it provides insights about how a model is operating, utilizing attention as the explanation of model predictions is still highly dubious. The community is still seeking more interpretable strategies for better identifying local active regions that contribute the most to the final decision. To improve the interpretability of existing attention models, we propose a novel Bilinear Representative Non-Parametric Attention (BR-NPA) strategy that captures the task-r…

从零到CAD：在百万级规模上无需真实数据合成可解释的CAD程序

Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data

本文提出Zero-to-CAD框架，通过代理搜索方法生成可执行的CAD构造序列，实现百万级可解释CAD程序的合成，并展示了其在多视图图像重建中的应用效果。

DCG-Net：双交叉注意力与概念-值图推理用于可解释的医学诊断

DCG-Net: Dual Cross-Attention with Concept-Value Graph Reasoning for Interpretable Medical Diagnosis

DCG-Net通过双交叉注意力和概念-值图推理，提升医学诊断的可解释性，实现白血球形态和皮肤病变诊断的高精度分类。

用参数化激活函数控制图像生成过程

Controlling the image generation process with parametric activation functions

本文提出一种通过替换生成网络中的激活函数为参数化形式并设置其参数来控制图像生成的方法，并在StyleGAN2和BigGAN上验证了其有效性。

A Parametric Top-View Representation of Complex Road Scenes

In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input. To achieve that, we first propose a novel parameterized model of road layouts in a top-view representation, which is not only intuitive for human visualization but also provides an interpretable interface for higher-level decision making. Moreover, the design of our top-view scene model allows for efficient sampling and thus generation of large-scale simulated data, which we leverage to train a deep neural network to infer our scene model's parameters. Specifically, our proposed…

面向音频驱动面部重演的参数化隐式人脸表征

Parametric Implicit Face Representation for Audio-Driven Facial Reenactment

针对音频驱动面部重演中显式与隐式表征的权衡问题，提出参数化隐式人脸表征及可控高质量重演框架，通过多项技术优化提升生成效果与泛化性，实验验证其在逼真度和身份风格保真度上优于现有方法。

DeepTaxon: 一种可解释的检索增强多模态框架，用于统一的物种识别与发现

DeepTaxon: An Interpretable Retrieval-Augmented Multimodal Framework for Unified Species Identification and Discovery

DeepTaxon通过可解释的检索增强多模态框架，统一物种识别与发现，通过检索证据进行链式推理，提升识别和发现的准确性与可解释性。

基于原型概念引导的 LoRA 专家混合的可解释少样本图像分类

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts

本文提出少样本原型概念分类框架 FSPCC，通过 LoRA 专家混合、跨模块概念引导、多层次特征保留和几何感知概念判别损失，缓解低数据下的参数不平衡与表示失配问题，在六个基准上以 4.2%-8.7% 的相对提升超越现有自解释模型。