ZMIS-SAM: Segment Anything Model Enhanced with Wavelet Transform for Zooplankton Microscopy Image Instance Segmentation
- URL: http://arxiv.org/abs/2607.27585v1
- Date: Thu, 30 Jul 2026 02:06:00 GMT
- Title: ZMIS-SAM: Segment Anything Model Enhanced with Wavelet Transform for Zooplankton Microscopy Image Instance Segmentation
- Abstract summary: We propose a novel instance segmentation model based on SAM and wavelet transform (ZMIS-SAM)<n>Our framework incorporates three core innovations: ZM-ViT enhances SAM's capability to model zooplankton morphology and image intensity distributions.<n>ZMIS-SAM achieves state-of-the-art instance segmentation performance on the zooplankton dataset.
- Score: 4.369116904792311
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As primary consumers in the marine food chain, zooplankton play a crucial role in maintaining marine ecological balance. However, the Segment Anything Model (SAM) exhibits limited performance in microscopic image instance segmentation due to its lack of zooplankton-specific domain knowledge. To address these challenges, we propose a novel instance segmentation model based on SAM and wavelet transform (ZMIS-SAM), effectively tackling issues such as inaccurate classification, discontinuous segmentation of slender appendages, and incomplete boundary segmentation. Our framework incorporates three core innovations: ZM-ViT enhances SAM's capability to model zooplankton morphology and image intensity distributions through two lightweight adapters, the Neighboring Feature Aggregation Module (NFAM) improves continuous segmentation of semi-transparent slender appendages by integrating general-purpose and domain-specific features, and the Wavelet-based Multi-scale Multi-directional Feature Enhancement (WM2FE) module effectively recovers high-frequency details to refine boundary segmentation completeness. Extensive experiments demonstrate that ZMIS-SAM achieves state-of-the-art instance segmentation performance on the zooplankton dataset and exhibits strong generalization capability across multiple public cross-domain datasets.
Related papers
- Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation [39.9598566181483]
Segment Anything Model (SAM) offers strong segmentation performance and generalization capabilities for RSISS tasks.<n>FE-SAM is a scalable and efficient framework for RSISS.<n>Experiments on three benchmark datasets demonstrate that FE-SAM outperforms state-of-the-art methods.
arXiv Detail & Related papers (2026-08-15T05:55:41Z) - Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement [24.433000105666114]
We propose a parameter-efficient fine-tuning framework, named Cooperative Fine-Grained Refinement of SAM.<n>These three components work cooperatively to enhance local perception, preserve spatial details, and refine boundaries, enabling SAM to perform accurate nuclei instance segmentation directly.
arXiv Detail & Related papers (2026-03-30T04:39:07Z) - HFP-SAM: Hierarchical Frequency Prompted SAM for Efficient Marine Animal Segmentation [61.044699537570644]
We propose a novel learning framework, named Hierarchical Frequency Prompted SAM (HFP-SAM) for high-performance Marine Animal segmentation.<n>First, we design a Frequency Guided Adapter (FGA) to efficiently inject marine scene information into the frozen SAM backbone.<n>We also introduce a Frequency-aware Point Selection (FPS) to generate highlighted regions through frequency analysis.
arXiv Detail & Related papers (2026-03-13T06:43:26Z) - Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset [76.92197418745822]
camouflaged instance segmentation (CIS) faces greater challenges in accurately segmenting objects that blend closely with their surroundings.<n>Traditional camouflaged instance segmentation methods, trained on terrestrial-dominated datasets with limited underwater samples, may exhibit inadequate performance in underwater scenes.<n>We introduce the first underwater camouflaged instance segmentation dataset, UCIS4K, which comprises 3,953 images of camouflaged marine organisms with instance-level annotations.
arXiv Detail & Related papers (2025-10-20T14:34:51Z) - TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation [20.89385225170904]
Segment Anything Model (SAM) has demonstrated impressive zero-shot segmentation capabilities across natural image domains.<n>We introduce TASAM, a terrain and temporally-aware extension of SAM designed specifically for high-resolution remote sensing image segmentation.
arXiv Detail & Related papers (2025-09-19T09:24:24Z) - Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts [17.6980007370549]
We make the first attempt to adapt Segment Anything Model (SAM) for multi-modal semantic segmentation.<n>By training only the MoE-LoRA layers while keeping SAM's weights frozen, SAM's strong generalization and segmentation capabilities can be preserved for downstream tasks.<n>Specifically, to address cross-modal inconsistencies, we propose a novel MoE routing strategy that adaptively generates weighted features across modalities.
arXiv Detail & Related papers (2024-12-05T14:54:31Z) - SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification [9.69491390062406]
We propose a novel MIL framework, named SAM-MIL, that emphasizes spatial contextual awareness and explicitly incorporates spatial context.
Our approach includes the design of group feature extraction based on spatial context and a SAM-Guided Group Masking strategy.
Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that our proposed SAM-MIL model outperforms existing mainstream methods in WSIs classification.
arXiv Detail & Related papers (2024-07-25T01:12:48Z) - Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM [62.85895749882285]
Marine Animal (MAS) involves segmenting animals within marine environments.
We propose a novel feature learning framework, named Dual-SAM for high-performance MAS.
Our proposed method achieves state-of-the-art performances on five widely-used MAS datasets.
arXiv Detail & Related papers (2024-04-07T15:34:40Z) - PosSAM: Panoptic Open-vocabulary Segment Anything [58.72494640363136]
PosSAM is an open-vocabulary panoptic segmentation model that unifies the strengths of the Segment Anything Model (SAM) with the vision-native CLIP model in an end-to-end framework.
We introduce a Mask-Aware Selective Ensembling (MASE) algorithm that adaptively enhances the quality of generated masks and boosts the performance of open-vocabulary classification during inference for each image.
arXiv Detail & Related papers (2024-03-14T17:55:03Z) - Semantic-SAM: Segment and Recognize Anything at Any Granularity [83.64686655044765]
We introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity.
We consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts.
For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels.
arXiv Detail & Related papers (2023-07-10T17:59:40Z) - Semantic Attention and Scale Complementary Network for Instance
Segmentation in Remote Sensing Images [54.08240004593062]
We propose an end-to-end multi-category instance segmentation model, which consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB)
SEA module contains a simple fully convolutional semantic segmentation branch with extra supervision to strengthen the activation of interest instances on the feature map.
SCMB extends the original single mask branch to trident mask branches and introduces complementary mask supervision at different scales.
arXiv Detail & Related papers (2021-07-25T08:53:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.