2 citations · 5 across the 7 of their papers we have counts for
8 papers
LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts
Wenxuan Guo, Yingping Liang, Zhaoguo Wang +11
Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, suboptimal behavior, and failures:…
GaussianToken: An Effective Image Tokenizer with 2D Gaussian Splatting
Jiajun Dong, Chengkun Wang, Wenzhao Zheng +3
Effective image tokenization is crucial for both multi-modal understanding and generation tasks due to the necessity of the alignment with discrete text data. To this end, existing…
V2M: Visual 2-Dimensional Mamba for Image Representation Learning
Chengkun Wang, Wenzhao Zheng, Yuanhui Huang +2
Mamba has garnered widespread attention due to its flexible design and efficient hardware performance to process 1D sequences based on the state space model (SSM). Recent studies h…
GlobalMamba: Global Image Serialization for Vision Mamba
Chengkun Wang, Wenzhao Zheng, Jie Zhou +1
Vision mambas have demonstrated strong performance with linear complexity to the number of vision tokens. Their efficiency results from processing image tokens sequentially. Howeve…
Introspective Deep Metric Learning
Chengkun Wang, Wenzhao Zheng, Zheng Zhu +2
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods focus on learning…
OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions
Chengkun Wang, Wenzhao Zheng, Zheng Zhu +2
The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning…
Probabilistic Deep Metric Learning for Hyperspectral Image Classification
Chengkun Wang, Wenzhao Zheng, Xian Sun +2
This paper proposes a probabilistic deep metric learning (PDML) framework for hyperspectral image classification, which aims to predict the category of each pixel for an image capt…
Introspective Deep Metric Learning for Image Retrieval
Wenzhao Zheng, Chengkun Wang, Jie Zhou +1
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods produce confident…