7 citations · 8 across the 11 of their papers we have counts for
7 papers · 1 filter
VISTA: A Visual Analytics Framework to Enhance Foundation Model-Generated Data Labels
Xiwei Xuan, Xiaoqi Wang, Wenbin He +4
The advances in multi-modal foundation models (FMs) (e.g., CLIP and LLaVA) have facilitated the auto-labeling of large-scale datasets, enhancing model performance in challenging do…
ReME: A Data-Centric Framework for Training-Free Open-Vocabulary Segmentation
Xiwei Xuan, Ziquan Deng, Kwan-Liu Ma
Training-free open-vocabulary semantic segmentation (OVS) aims to segment images given a set of arbitrary textual categories without costly model fine-tuning. Existing solutions of…
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Xinyuan Yan, Xiwei Xuan, Jorge Piazentin Ono +6
Real-world machine learning models require rigorous evaluation before deployment, especially in safety-critical domains like autonomous driving and surveillance. The evaluation of…
SLIM: Spuriousness Mitigation with Minimal Human Annotations
Xiwei Xuan, Ziquan Deng, Hsuan-Tien Lin +1
Recent studies highlight that deep learning models often learn spurious features mistakenly linked to labels, compromising their reliability in real-world scenarios where such corr…
USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation
Xiaoqi Wang, Wenbin He, Xiwei Xuan +8
The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recen…
AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding
Xiwei Xuan, Jorge Piazentin Ono, Liang Gou +2
Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance, often cha…
SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective
Xiwei Xuan, Ziquan Deng, Hsuan-Tien Lin +2
Researchers have proposed various methods for visually interpreting the Convolutional Neural Network (CNN) via saliency maps, which include Class-Activation-Map (CAM) based approac…