克服3D手形重建中准确性与合理性之间的权衡

描述符汤与词汤：克服分布外少样本学习中参数效率与准确率的权衡

Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning

本文提出描述符汤和词汤两种少样本方法，无需测试时LLM且不需反向传播，以更少参数和集成成员在OOD少样本学习中超越现有方法。

Towards Both Accurate and Robust Neural Networks without Extra Data

Deep neural networks have achieved remarkable performance in various applications but are extremely vulnerable to adversarial perturbation. The most representative and promising methods that can enhance model robustness, such as adversarial training and its variants, substantially degrade model accuracy on benign samples, limiting practical utility. Although incorporating extra training data can alleviate the trade-off to a certain extent, it remains unsolved to achieve both robustness and accuracy under limited training data. Here, we demonstrate the feasibility of overcoming the trade-off, b…

Topology-Adaptive Mesh Deformation for Surface Evolution, Morphing, and Multi-View Reconstruction

Triangulated meshes have become ubiquitous discrete-surface representations. In this paper we address the problem of how to maintain the manifold properties of a surface while it undergoes strong deformations that may cause topological changes. We introduce a new self-intersection removal algorithm, TransforMesh, and we propose a mesh evolution framework based on this algorithm. Numerous shape modelling applications use surface evolution in order to improve shape properties, such as appearance or accuracy. Both explicit and implicit representations can be considered for that purpose. However, …