most cited1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

12 citations · 35 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2022

Learning to Learn Better for Video Object Segmentation

Meng Lan, Jing Zhang, Lefei Zhang +1

Recently, the joint learning framework (JOINT) integrates matching based transductive reasoning and online inductive learning to achieve accurate and robust semi-supervised video o…

cs.CV202212 cited

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

Benjamin Kiefer, Matej Kristan, Janez Perš +70

The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…

cs.CV20228 cited

Unified Discrete Diffusion for Simultaneous Vision-Language Generation

Minghui Hu, Chuanxia Zheng, Heliang Zheng +5

The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In thi…

cs.CV2022

Rethinking Hierarchies in Pre-trained Plain Vision Transformer

Yufei Xu, Jing Zhang, Qiming Zhang +1

Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been proven very effective. However, customized algorithms should be carefully designed fo…

cs.CV20222 cited

HL-Net: Heterophily Learning Network for Scene Graph Generation

Xin Lin, Changxing Ding, Yibing Zhan +2

Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural networks (GNNs) to…

cs.CV20223 cited

RU-Net: Regularized Unrolling Network for Scene Graph Generation

Xin Lin, Changxing Ding, Jing Zhang +2

Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects. Existing SGG methods usually suffer from several issues, including 1…