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Tingjin Chu

3 accepted papers

2025

LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning

ICLR 2025poster

Low-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may constrain the optimization flexibility. To address this limitation, we introduce Location-aware Cosine Adaptation (LoCA), a…

Cited by 0SourcePDFScholar
2024

In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment

NeurIPS 2024poster

Neural representations for 3D scenes have made substantial advancements recently, yet object removal remains a challenging yet practical issue, due to the absence of multi-view supervision over occluded areas. Diffusion Models (DMs), trained on extensive 2D images, show diverse and high-fidelity ge…

Cited by 0SourcePDFScholar
2022

Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression

ECCV 2022poster

"Monocular Depth Estimation (MDE) is a task to predict a dense depth map from a single image. Despite the recent progress brought by deep learning, existing methods are still prone to errors due to the ill-posed nature of MDE. Hence depth estimation systems must be self-aware of possible mistakes to…