📝 发表论文

Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset

Shijie Lian, Ziyi Zhang, Hua Li*, Wenjie Li, Laurence Tianruo Yang, Sam Kwong, Runmin Cong.

Code

• This paper introduces the first large-scale underwater salient instance segmentation dataset (USIS10K) and proposes the USIS-SAM architecture based on the Segment Anything Model (SAM) for underwater vision tasks. The model incorporates an Underwater Adaptive Visual Transformer (UA-ViT) and an automatic Salient Feature Prompter Generator (SFPG) to improve segmentation accuracy in complex underwater environments.

FSNet: Frequency Domain Guided Superpixel Segmentation Network for Complex Scenes

Hua Li, Junyan Liang, Wenjie Li, Wenhui Wu*

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• This paper proposes FSNet, an end-to-end frequency domain guided superpixel segmentation network designed for complex scenes. FSNet generates superpixels with sharp boundary adherence by fusing deep features from both spatial and frequency domains. An improved frequency information extractor (IFIE) captures frequency domain details with sharp boundary features, while a dense hybrid atrous convolution (DHAC) block preserves semantic information by capturing broader and deeper spatial features. The fusion of these features enables the generation of semantic perceptual superpixels with enhanced boundary accuracy.

HCMANet: Hierarchical Cross-Modality Attention Network for Underwater Salient Object Detection

Yu Wang, Wenjie Li, Zhiyang Yu, Yi Xue, Hua Li*

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• This paper proposes a novel method for parameter estimation and tracking of an expandable cable-driven parallel mechanism in unknown environments. Based solely on measurements of cable length variations, the authors optimize to infer both the frame shape and cable anchor positions, then demonstrate the mechanism’s ability to track and steer with submillimeter accuracy through both simulations and experiments.