7 citations · 15 across the 20 of their papers we have counts for
12 papers · 1 filter
Data Distribution Valuation
Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo +2
Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a…
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
Yongyi Su, Yushu Li, Nanqing Liu +4
Test-time adaptation (TTA) updates the model weights during the inference stage using testing data to enhance generalization. However, this practice exposes TTA to adversarial risk…
Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs
Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao +3
Protecting intellectual property (IP) of text such as articles and code is increasingly important, especially as sophisticated attacks become possible, such as paraphrasing by larg…
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation
Mohamed Ragab, Peiliang Gong, Emadeldeen Eldele +6
Source-free domain adaptation (SFDA) aims to adapt a model pre-trained on a labeled source domain to an unlabeled target domain without access to source data, preserving the source…
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
Jingtan Wang, Xiaoqiang Lin, Rui Qiao +2
The increasing complexity of foundational models underscores the necessity for explainability, particularly for fine-tuning, the most widely used training method for adapting model…
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training
Wenyu Zhang, Li Shen, Chuan-Sheng Foo
Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to a related but unlabeled target domain. While the source model is a key…
REACTO: Reconstructing Articulated Objects from a Single Video
Chaoyue Song, Jiacheng Wei, Chuan-Sheng Foo +2
In this paper, we address the challenge of reconstructing general articulated 3D objects from a single video. Existing works employing dynamic neural radiance fields have advanced…
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks
Brian Formento, Wenjie Feng, Chuan Sheng Foo +2
Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern. While current research has ex…
Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias
Wenyu Zhang, Qingmu Liu, Felix Ong Wei Cong +2
Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain. In this wor…
Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior
Cheng Chen, Xiaofeng Yang, Fan Yang +5
Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g., faces on the bac…
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection
Jingyi Liao, Xun Xu, Manh Cuong Nguyen +2
Existing approaches towards anomaly detection~(AD) often rely on a substantial amount of anomaly-free data to train representation and density models. However, large anomaly-free d…
Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction
Kennard Yanting Chan, Fayao Liu, Guosheng Lin +2
Pixel-aligned implicit models, such as PIFu, PIFuHD, and ICON, are used for single-view clothed human reconstruction. These models need to be trained using a sampling training sche…