15 citations · 25 across the 21 of their papers we have counts for
22 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…
Domain Generalization via Discrete Codebook Learning
Shaocong Long, Qianyu Zhou, Xikun Jiang +3
Domain generalization (DG) strives to address distribution shifts across diverse environments to enhance model's generalizability. Current DG approaches are confined to acquiring r…
Generative Classifier for Domain Generalization
Shaocong Long, Qianyu Zhou, Xiangtai Li +7
Domain generalization (DG) aims to improve the generalizability of computer vision models toward distribution shifts. The mainstream DG methods predominantly focus on learning doma…
Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs
Yikang Zhou, Tao Zhang, Shilin Xu +7
Recent advancements in multimodal large language models (MLLM) have shown a strong ability in visual perception, reasoning abilities, and vision-language understanding. However, th…
StyleRWKV: High-Quality and High-Efficiency Style Transfer with RWKV-like Architecture
Miaomiao Dai, Qianyu Zhou, Lizhuang Ma
Style transfer aims to generate a new image preserving the content but with the artistic representation of the style source. Most of the existing methods are based on Transformers…
DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion
Yinghui Li, Qianyu Zhou, Jingyu Gong +4
Point Transformers (PoinTr) have shown great potential in point cloud completion recently. Nevertheless, effective domain adaptation that improves transferability toward target dom…
Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis
Jingyu Gong, Chong Zhang, Fengqi Liu +5
Scene-aware motion synthesis has been widely researched recently due to its numerous applications. Prevailing methods rely heavily on paired motion-scene data, while it is difficul…
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…
Source-Free Test-Time Adaptation For Online Surface-Defect Detection
Yiran Song, Qianyu Zhou, Lizhuang Ma
Surface defect detection is significant in industrial production. However, detecting defects with varying textures and anomaly classes during the test time is challenging. This ari…
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…
MotionBooth: Motion-Aware Customized Text-to-Video Generation
Jianzong Wu, Xiangtai Li, Yanhong Zeng +5
In this work, we present MotionBooth, an innovative framework designed for animating customized subjects with precise control over both object and camera movements. By leveraging a…
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…
Continuous Piecewise-Affine Based Motion Model for Image Animation
Hexiang Wang, Fengqi Liu, Qianyu Zhou +3
Image animation aims to bring static images to life according to driving videos and create engaging visual content that can be used for various purposes such as animation, entertai…
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…