10 citations · 10 across the 16 of their papers we have counts for
18 papers
ExpandDiff: Dynamic Range Expanding Diffusion for Single-Image HDR Reconstruction
Mehmet Emre andıran, Mehmet Emre Andıran, Zhuoqian Yang +3
Single-image HDR reconstruction requires inferring missing detail while preserving the visible content of an LDR image. Differences in sensor dynamic range and exposure cause LDR i…
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…
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-…
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…
Self-Ensembling Gaussian Splatting for Few-Shot Novel View Synthesis
Chen Zhao, Xuan Wang, Tong Zhang +2
3D Gaussian Splatting (3DGS) has demonstrated remarkable effectiveness in novel view synthesis (NVS). However, 3DGS tends to overfit when trained with sparse views, limiting its ge…
Unlocking Comics: The AI4VA Dataset for Visual Understanding
Peter Grönquist, Deblina Bhattacharjee, Bahar Aydemir +4
In the evolving landscape of deep learning, there is a pressing need for more comprehensive datasets capable of training models across multiple modalities. Concurrently, in digital…
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…
Data Augmentation via Latent Diffusion for Saliency Prediction
Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang +2
Saliency prediction models are constrained by the limited diversity and quantity of labeled data. Standard data augmentation techniques such as rotating and cropping alter scene co…
SINDER: Repairing the Singular Defects of DINOv2
Haoqi Wang, Tong Zhang, Mathieu Salzmann
Vision Transformer models trained on large-scale datasets, although effective, often exhibit artifacts in the patch token they extract. While such defects can be alleviated by re-t…
Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density
Shuangqi Li, Chen Liu, Tong Zhang +3
We introduce an approach to bias deep generative models, such as GANs and diffusion models, towards generating data with either enhanced fidelity or increased diversity. Our approa…
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…
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…
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange
Yanhao Wu, Tong Zhang, Wei Ke +3
In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely…
OMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation
Baran Ozaydin, Tong Zhang, Deblina Bhattacharjee +2
Unsupervised Semantic Segmentation (USS) involves segmenting images without relying on predefined labels, aiming to alleviate the burden of extensive human labeling. Existing metho…
DVMNet++: Rethinking Relative Pose Estimation for Unseen Objects
Chen Zhao, Tong Zhang, Zheng Dang +1
Determining the relative pose of a previously unseen object between two images is pivotal to the success of generalizable object pose estimation. Existing approaches typically pred…
Adaptive Multi-step Refinement Network for Robust Point Cloud Registration
Zhi Chen, Yufan Ren, Tong Zhang +4
Point Cloud Registration (PCR) estimates the relative rigid transformation between two point clouds of the same scene. Despite significant progress with learning-based approaches,…
SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
Haobo Jiang, Mathieu Salzmann, Zheng Dang +2
In this paper, we introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D…
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration
Zheng Dang, Mathieu Salzmann
In the current deep learning paradigm, the amount and quality of training data are as critical as the network architecture and its training details. However, collecting, processing…