23 citations · 46 across the 14 of their papers we have counts for
14 papers
TaskAnchor: Grounding Task State in Reactive VLAs for Long-Horizon Manipulation
Hengyan Liu, Wenlve Zhou, Bo Yue +7
Reactive vision-language-action (VLA) policies suffer from task-state aliasing in long-horizon manipulation, where identical multimodal inputs call for distinct, context-dependent…
HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enhancement
Yiyang Cai, Nan Chen, Rongchang Xie +8
Human-object centric video personalization (HOCVP) is a core task within subject-driven video generation. However, existing methods suffer from two key limitations. First, most app…
DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation
Nan Chen, Yiyang Cai, Rongchang Xie +7
Open domain subject-driven text-to-video (S2V) generation has drawn significant interest in academia and industry. Open domain S2V mainly involves two scenarios: in-domain, which r…
PhysDrift: Bridging the Embodiment Gap in Humanoid Co-Speech Motion Generation
Zhangzhao Liang, Xiaofen Xing, Mingyue Yang +2
Humanoid robots require co-speech motions that are not only expressive and speech-aligned, but also physically executable under embodiment constraints. Existing co-speech generatio…
Dexterity-BEV: Aligning 3D World and Actions for Generalizable Robot Policies Learning
Huayi Zhou, Wei Gao, Dekun Lu +12
End-to-end manipulation policies, combined with web-scale pretrained Vision-Language Models (VLMs), show the promise for generalizable and dexterous robotic manipulation. However,…
Pano360: Perspective to Panoramic Vision with Geometric Consistency
Zhengdong Zhu, Weiyi Xue, Zuyuan Yang +2
Prior panorama stitching approaches heavily rely on pairwise feature correspondences and are unable to leverage geometric consistency across multiple views. This leads to severe di…
Consistency-Regularized GAN for Few-Shot SAR Target Recognition
Yikui Zhai, Shikuang Liu, Wenlve Zhou +4
Few-shot recognition in synthetic aperture radar (SAR) imagery remains a critical bottleneck for real-world applications due to extreme data scarcity. A promising strategy involves…
Beyond Weight Adaptation: Feature-Space Domain Injection for Cross-Modal Ship Re-Identification
Tingfeng Xian, Wenlve Zhou, Zhiheng Zhou +1
Cross-Modality Ship Re-Identification (CMS Re-ID) is critical for achieving all-day and all-weather maritime target tracking, yet it is fundamentally challenged by significant moda…
Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection
Yijun Zhou, Yikui Zhai, Zilu Ying +7
Although deep learning has advanced remote sensing change detection (RSCD), most methods rely solely on image modality, limiting feature representation, change pattern modeling, an…
Masked Representation Modeling for Domain-Adaptive Segmentation
Wenlve Zhou, Zhiheng Zhou, Tiantao Xian +3
Unsupervised domain adaptation (UDA) for semantic segmentation seeks to transfer models from a labeled source domain to an unlabeled target domain. While auxiliary self-supervised…
BiPC: Bidirectional Probability Calibration for Unsupervised Domain Adaption
Wenlve Zhou, Zhiheng Zhou, Junyuan Shang +4
Unsupervised Domain Adaptation (UDA) leverages a labeled source domain to solve tasks in an unlabeled target domain. While Transformer-based methods have shown promise in UDA, thei…
Weakly Contrastive Learning via Batch Instance Discrimination and Feature Clustering for Small Sample SAR ATR
Yikui Zhai, Wenlve Zhou, Bing Sun +8
In recent years, impressive performance of deep learning technology has been recognized in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). Since a large amount o…
Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
Wenlve Zhou, Zhiheng Zhou
This paper addresses two vital challenges in Unsupervised Domain Adaptation (UDA) with a focus on harnessing the power of Vision-Language Pre-training (VLP) models. Firstly, UDA ha…
Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation
Wenlve Zhou, Zhiheng Zhou, Tianlei Wang +1
Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source domain to perform well on a target domain without requiring additional annotations. In the conte…