343 citations · 373 across the 11 of their papers we have counts for
12 papers
AC^2-VLA: Action-Context-Aware Adaptive Computation in Vision-Language-Action Models for Efficient Robotic Manipulation
Wenda Yu, Tianshi Wang, Fengling Li +2
Vision-Language-Action (VLA) models have demonstrated strong performance in robotic manipulation, yet their closed-loop deployment is hindered by the high latency and compute cost…
Generalizing vision-language models to novel domains: A comprehensive survey
Xinyao Li, Jingjing Li, Fengling Li +3
Recently, vision-language pretraining has emerged as a transformative technique that integrates the strengths of both visual and textual modalities, resulting in powerful vision-la…
Agile Multi-Source-Free Domain Adaptation
Xinyao Li, Jingjing Li, Fengling Li +2
Efficiently utilizing rich knowledge in pretrained models has become a critical topic in the era of large models. This work focuses on adaptively utilizing knowledge from multiple…
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
Tianshi Wang, Fengling Li, Lei Zhu +3
With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users seeking access to data across various modalities…
Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment
Jinghan Ru, Jun Tian, Zhekai Du +3
Multimedia applications are often associated with cross-domain knowledge transfer, where Unsupervised Domain Adaptation (UDA) can be used to reduce the domain shifts. Open Set Doma…
A Comprehensive Survey on Source-free Domain Adaptation
Zhiqi Yu, Jingjing Li, Zhekai Du +2
Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the…
Domain Adaptive Semantic Segmentation without Source Data
Fuming You, Jingjing Li, Lei Zhu +3
Domain adaptive semantic segmentation is recognized as a promising technique to alleviate the domain shift between the labeled source domain and the unlabeled target domain in many…
Two-pronged Strategy: Lightweight Augmented Graph Network Hashing for Scalable Image Retrieval
Hui Cui, Lei Zhu, Jingjing Li +2
Hashing learns compact binary codes to store and retrieve massive data efficiently. Particularly, unsupervised deep hashing is supported by powerful deep neural networks and has th…
Adversarial Energy Disaggregation for Non-intrusive Load Monitoring
Zhekai Du, Jingjing Li, Lei Zhu +2
Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual…
Semantics Disentangling for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Ruihong Qiu +4
Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training. To bridge the gap between the seen and uns…
Maximum Density Divergence for Domain Adaptation
Li Jingjing, Chen Erpeng, Ding Zhengming +3
Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctiv…
Locality Preserving Joint Transfer for Domain Adaptation
Li Jingjing, Jing Mengmeng, Lu Ke +2
Domain adaptation aims to leverage knowledge from a well-labeled source domain to a poorly-labeled target domain. A majority of existing works transfer the knowledge at either feat…