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Meijun Sun

7 accepted papers

2025

Boosting Lightweight Camouflaged Object Detection with Multi-Scale Context and Boundary Awareness

ICASSP 2025accepted

To adapt to the resource-limited environment, this study introduces the lightweight boundary-aware camouflaged object detection(COD) network LMABnet. We enhance the feature representation capability of the lightweight network through a multi-scale feature fusion architecture, while effectively avoid…

Cited by 0SourceScholar
2025

Certainty-guided Reasoning and Refinement Network for Camouflaged Object Detection

ICASSP 2025accepted

Camouflaged object detection (COD), which aims to segment objects that are highly similar to their background, is a valuable yet challenging task. Due to the interference of clutter and noise in the background, existing methods often struggle to avoid misleading and accurately segment the camouflage…

Cited by 0SourceScholar
2025

Joint Edge and Regional Depth Enhancement Network for Camouflaged Object Detection

ICASSP 2025accepted

Camouflaged object detection (COD) is a task of identifying and locating target objects that are camouflaged, masked, or confused. Research claims that depth cues can provide effective object location cues. However, depth images often contain noise interference, which may negatively affect object re…

Cited by 0SourceScholar
2025

LawDIS: Language-Window-based Controllable Dichotomous Image Segmentation

ICCV 2025poster

We present LawDIS, a language-window-based controllable dichotomous image segmentation (DIS) framework that produces high-quality object masks. Our framework recasts DIS as an image-conditioned mask generation task within a latent diffusion model, enabling seamless integration of user controls. LawD…

2025

Multi-Task Joint 3D Swin Transformer Learning for Segmentation and Classification of Hyperspectral Medicine Images

ICASSP 2025accepted

Hyperspectral images had made many applications in the medical field with their rich spectral information. However, there were currently problems with feature extraction based on hyperspectral images, especially in extracting contextual feature information from spectral bands, and a single convoluti…

Cited by 0SourceScholar
2025

Multi-scale Re-weighted Attention Feature Fusion for Non-Intrusive Load Monitoring

ICASSP 2025accepted

Non-Intrusive Load Monitoring (NILM) addresses the challenge of disaggregating total energy consumption into individual appliance usage, which is essential for enhancing energy efficiency and managing smart grids. Existing methods often overlook the impact of window sizes on the separation of applia…

Cited by 0SourceScholar
2020

Extract and Merge: Superpixel Segmentation with Regional Attributes

ECCV 2020poster

For a certain object in an image, the relationship between its central region and the peripheral region is not well utilized in existing superpixel segmentation methods. In this work, we propose the concept of regional attribute, which indicates the location of a certain region in the object. Based…

Cited by 3SourcePDFScholar