9 papers
Weight Space Representation Learning via Neural Field Adaptation
Zhuoqian Yang, Mathieu Salzmann, Sabine Süsstrunk
We investigate the potential of weights to serve as effective representations, focusing on neural fields. Our key insight is that constraining the optimization space through a pre-…
Coherent and Multi-modality Image Inpainting via Latent Space Optimization
Lingzhi Pan, Tong Zhang, Bingyuan Chen +4
With the advancements in denoising diffusion probabilistic models (DDPMs), image inpainting has significantly evolved from merely filling information based on nearby regions to gen…
Subtractive Modulative Network with Learnable Periodic Activations
Tiou Wang, Zhuoqian Yang, Markus Flierl +2
We propose the Subtractive Modulative Network (SMN), a novel, parameter-efficient Implicit Neural Representation (INR) architecture inspired by classical subtractive synthesis. The…
OpenMaterial: A Large-scale Dataset of Complex Materials for 3D Reconstruction
Zheng Dang, Jialu Huang, Fei Wang +1
Recent advances in deep learning, such as neural radiance fields and implicit neural representations, have significantly advanced 3D reconstruction. However, accurately reconstruct…
Demystifying Singular Defects in Large Language Models
Haoqi Wang, Tong Zhang, Mathieu Salzmann
Large transformer models are known to produce high-norm tokens. In vision transformers (ViTs), such tokens have been mathematically modeled through the singular vectors of the line…
QT-DoG: Quantization-aware Training for Domain Generalization
Saqib Javed, Hieu Le, Mathieu Salzmann
A key challenge in Domain Generalization (DG) is preventing overfitting to source domains, which can be mitigated by finding flatter minima in the loss landscape. In this work, we…