3 citations · 7 across the 17 of their papers we have counts for
21 papers
ULIFLOW: Unified Latent Human-Object Interaction Flow
Wenqi Cai, Chunzhi Gu, Weichen Zhou +5
Text-conditioned 3D human--object interaction (HOI) generation remains challenging because individually plausible human and object motions do not necessarily form a coherent intera…
ProMoDD: Dataset Distillation for 3D Human Motion via Prototype-Conditioned Diffusion Models
Yijin Wei, Yawen Zou, Wenqi Cai +5
Dataset distillation (DD) aims to retain the training utility of a large dataset in a much smaller set of examples. For skeleton-based action recognition, this requires capturing d…
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…
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…
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…
Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework
Koichiro Kamide, Shunsuke Sakai, Shun Maeda +2
Human Action Anomaly Detection (HAAD) aims to identify anomalous actions given only normal action data during training. Existing methods typically follow a one-model-per-category p…
Label-Consistent Dataset Distillation with Detector-Guided Refinement
Yawen Zou, Guang Li, Zi Wang +2
Dataset distillation (DD) aims to generate a compact yet informative dataset that achieves performance comparable to the original dataset, thereby reducing demands on storage and c…
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…
Incremental Pseudo-Labeling for Black-Box Unsupervised Domain Adaptation
Yawen Zou, Chunzhi Gu, Jun Yu +2
Black-Box unsupervised domain adaptation (BBUDA) learns knowledge only with the prediction of target data from the source model without access to the source data and source model,…
Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows
Shun Maeda, Chunzhi Gu, Jun Yu +3
We introduce the task of human action anomaly detection (HAAD), which aims to identify anomalous motions in an unsupervised manner given only the pre-determined normal category of…
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection
Jianjian Qin, Chunzhi Gu, Jun Yu +1
Anomaly detection (AD) is a fundamental task in computer vision. It aims to identify incorrect image data patterns which deviate from the normal ones. Conventional methods generall…
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…
Orientation-Aware Leg Movement Learning for Action-Driven Human Motion Prediction
Chunzhi Gu, Chao Zhang, Shigeru Kuriyama
The task of action-driven human motion prediction aims to forecast future human motion based on the observed sequence while respecting the given action label. It requires modeling…
A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization
Yu Ding, Jun Yu, Chunzhi Gu +2
Artificial neural networks (ANNs), inspired by the interconnection of real neurons, have achieved unprecedented success in various fields such as computer vision and natural langua…
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples
Jianjian Qin, Chunzhi Gu, Jun Yu +1
Anomaly detection, which is a critical and popular topic in computer vision, aims to detect anomalous samples that are different from the normal (i.e., non-anomalous) ones. The cur…
Learning Disentangled Representations for Controllable Human Motion Prediction
Chunzhi Gu, Jun Yu, Chao Zhang
Generative model-based motion prediction techniques have recently realized predicting controlled human motions, such as predicting multiple upper human body motions with similar lo…
Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder
Chunzhi Gu, Shuofeng Zhao, Chao Zhang
In this paper, we present a deep generative model based method to generate diverse human motion interpolation results. We resort to the Conditional Variational Auto-Encoder (CVAE)…
Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks
Chunzhi Gu, Yan Zhao, Chao Zhang
Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input. However, in real applications, a complete and correct past m…
Example-based Color Transfer with Gaussian Mixture Modeling
Chunzhi Gu, Xuequan Lu, Chao Zhang
Color transfer, which plays a key role in image editing, has attracted noticeable attention recently. It has remained a challenge to date due to various issues such as time-consumi…
Blur Removal via Blurred-Noisy Image Pair
Chunzhi Gu, Xuequan Lu, Ying He +1
Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images. In this paper, we propose a novel ima…