8 papers
Dataset Distillation Based on Saliency-Driven Prototype Alignment
Yawen Zou, Wenqi Cai, Guang Li +3
Dataset distillation aims to synthesize compact datasets that can approximate the performance of full-data training while significantly reducing computational and storage costs. Ho…
Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention
Weichen Zhou, Yawen Zou, Chunzhi Gu +3
We introduce a controlled subspace intervention framework to investigate how self-supervised Vision Transformers (ViTs) encode dense geometric information. While linear probing is…
Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset
Jianjian Qin, Chao Zhang, Chunzhi Gu +5
We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain. It is motivated by the fact that 2D-only representations in the AD task ma…
VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction
Hiroto Nakata, Yawen Zou, Shunsuke Sakai +5
Logical anomaly detection in industrial inspection remains challenging due to variations in visual appearance (e.g., background clutter, illumination shift, and blur), which often…
InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Models
Shunsuke Sakai, Xiangteng He, Chunzhi Gu +2
Despite the remarkable success, recent reconstruction-based anomaly detection (AD) methods via diffusion modeling still involve fine-grained noise-strength tuning and computational…
EVLF: Early Vision-Language Fusion for Generative Dataset Distillation
Wenqi Cai, Yawen Zou, Guang Li +2
Dataset distillation (DD) aims to synthesize compact training sets that enable models to achieve high accuracy with significantly fewer samples. Recent diffusion-based DD methods c…