77 citations · 121 across the 17 of their papers we have counts for
17 papers
Mamba Learns in Context: Structure-Aware Domain Generalization for Multi-Task Point Cloud Understanding
Jincen Jiang, Qianyu Zhou, Yuhang Li +5
While recent Transformer and Mamba architectures have advanced point cloud representation learning, they are typically developed for single-task or single-domain settings. Directly…
DAPointMamba: Domain Adaptive Point Mamba for Point Cloud Completion
Yinghui Li, Qianyu Zhou, Di Shao +4
Domain adaptive point cloud completion (DA PCC) aims to narrow the geometric and semantic discrepancies between the labeled source and unlabeled target domains. Existing methods ei…
PointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification
Hao Yang, Qianyu Zhou, Haijia Sun +4
Domain Generalization (DG) has been recently explored to enhance the generalizability of Point Cloud Classification (PCC) models toward unseen domains. Prior works are based on con…
Learning Adaptive Node Selection with External Attention for Human Interaction Recognition
Chen Pang, Xuequan Lu, Qianyu Zhou +1
Most GCN-based methods model interacting individuals as independent graphs, neglecting their inherent inter-dependencies. Although recent approaches utilize predefined interaction…
MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution
Zhiwen Shao, Yifan Cheng, Feiran Li +4
Facial micro-expression recognition (MER) is a challenging problem, due to transient and subtle micro-expression (ME) actions. Most existing methods depend on hand-crafted features…
DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers
Zitong Wang, Hang Zhao, Qianyu Zhou +3
Diffusion models have recently motivated great success in many generation tasks like object removal. Nevertheless, existing image decomposition methods struggle to disentangle semi…
PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding
Jincen Jiang, Qianyu Zhou, Yuhang Li +6
In this paper, we present PCoTTA, an innovative, pioneering framework for Continual Test-Time Adaptation (CoTTA) in multi-task point cloud understanding, enhancing the model's tran…
Emphasizing Semantic Consistency of Salient Posture for Speech-Driven Gesture Generation
Fengqi Liu, Hexiang Wang, Jingyu Gong +5
Speech-driven gesture generation aims at synthesizing a gesture sequence synchronized with the input speech signal. Previous methods leverage neural networks to directly map a comp…
PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model
Hao Yang, Qianyu Zhou, Haijia Sun +5
Domain Generalization (DG) has been recently explored to improve the generalizability of point cloud classification (PCC) models toward unseen domains. However, they often suffer f…
DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understanding
Jincen Jiang, Qianyu Zhou, Yuhang Li +5
Recent point cloud understanding research suffers from performance drops on unseen data, due to the distribution shifts across different domains. While recent studies use Domain Ge…
Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models
Fengfan Zhou, Qianyu Zhou, Hefei Ling +1
Adversarial attacks on Face Recognition (FR) systems have demonstrated significant effectiveness against standalone FR models. However, their practicality diminishes in complete FR…
DGMamba: Domain Generalization via Generalized State Space Model
Shaocong Long, Qianyu Zhou, Xiangtai Li +5
Domain generalization~(DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers…
Test-Time Domain Generalization for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +3
Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems against presentation attacks. While domain generalization (DG) methods have been developed to enhance…
SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes
Yiran Song, Qianyu Zhou, Xuequan Lu +2
Segment anything model (SAM) has demonstrated excellent generalizability in common vision scenarios, yet falling short of the ability to understand specialized data. Recently, seve…
BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model
Yiran Song, Qianyu Zhou, Xiangtai Li +3
In this paper, we address the challenge of image resolution variation for the Segment Anything Model (SAM). SAM, known for its zero-shot generalizability, exhibits a performance de…
Generalized Category Discovery in Semantic Segmentation
Zhengyuan Peng, Qijian Tian, Jianqing Xu +5
This paper explores a novel setting called Generalized Category Discovery in Semantic Segmentation (GCDSS), aiming to segment unlabeled images given prior knowledge from a labeled…
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
Qianyu Zhou, Zhengyang Feng, Qiqi Gu +4
Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Most existing methods suffer from noticeabl…