61 citations · 110 across the 12 of their papers we have counts for
12 papers
Adapting Segment Anything Model for Unseen Object Instance Segmentation
Rui Cao, Chuanxin Song, Biqi Yang +3
Unseen Object Instance Segmentation (UOIS) is crucial for autonomous robots operating in unstructured environments. Previous approaches require full supervision on large-scale tabl…
UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views
Jiaxin Guo, Jiangliu Wang, Ruofeng Wei +3
Visualizing surgical scenes is crucial for revealing internal anatomical structures during minimally invasive procedures. Novel View Synthesis is a vital technique that offers geom…
SR-Mamba: Effective Surgical Phase Recognition with State Space Model
Rui Cao, Jiangliu Wang, Yun-Hui Liu
Surgical phase recognition is crucial for enhancing the efficiency and safety of computer-assisted interventions. One of the fundamental challenges involves modeling the long-dista…
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene Reconstruction
Jiaxin Guo, Jiangliu Wang, Di Kang +3
Real-time 3D reconstruction of surgical scenes plays a vital role in computer-assisted surgery, holding a promise to enhance surgeons' visibility. Recent advancements in 3D Gaussia…
Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement
Yiang Lu, Bin Li, Wei Chen +6
Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However,…
Ada-Tracker: Soft Tissue Tracking via Inter-Frame and Adaptive-Template Matching
Jiaxin Guo, Jiangliu Wang, Zhaoshuo Li +3
Soft tissue tracking is crucial for computer-assisted interventions. Existing approaches mainly rely on extracting discriminative features from the template and videos to recover c…
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training
Jiangliu Wang, Jianbo Jiao, Yibing Song +5
This work aims to improve unsupervised audio-visual pre-training. Inspired by the efficacy of data augmentation in visual contrastive learning, we propose a novel speed co-augmenta…
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning
Chongjian Ge, Jiangliu Wang, Zhan Tong +3
Contrastive learning methods train visual encoders by comparing views from one instance to others. Typically, the views created from one instance are set as positive, while views f…
A Multi-Sensor Interface to Improve the Learning Experience in Arc Welding Training Tasks
Hoi-Yin Lee, Peng Zhou, Anqing Duan +3
This paper presents the development of a multi-sensor user interface to facilitate the instruction of arc welding tasks. Traditional methods to acquire hand-eye coordination skills…
Self-supervised Video Representation Learning by Pace Prediction
Jiangliu Wang, Jianbo Jiao, Yun-Hui Liu
This paper addresses the problem of self-supervised video representation learning from a new perspective -- by video pace prediction. It stems from the observation that human visua…
Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
Jiangliu Wang, Jianbo Jiao, Linchao Bao +3
This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of s…
Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics
Jiangliu Wang, Jianbo Jiao, Linchao Bao +3
We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using vi…