Segment Anything Model: Unleashing Next-Gen Perception Across Diverse Modalities and Quality

Latest 13 papers on segment anything model: Jan. 10, 2026

The Segment Anything Model (SAM) has rapidly become a cornerstone in computer vision, offering unprecedented generalization capabilities for image segmentation. Its “segment anything” ethos has inspired a wave of innovation, pushing boundaries in diverse fields from medical diagnostics to remote sensing. The challenge, however, lies in adapting this powerful foundation model to the complexities of real-world data – be it low-quality images, specialized modalities like hyperspectral or SAR, or domain-specific tasks requiring nuanced understanding. Recent research showcases exciting breakthroughs that address these very challenges, transforming SAM into an even more versatile and robust tool.

The Big Ideas & Core Innovations

At the heart of these advancements is a collective effort to bridge performance gaps and enhance SAM’s adaptability. A recurring theme is the integration of domain-specific cues and enhanced contextual understanding. For instance, in camouflaged object detection, where targets blend seamlessly with their surroundings, two papers offer compelling solutions. DGA-Net: Enhancing SAM with Depth Prompting and Graph-Anchor Guidance for Camouflaged Object Detection by researchers at University of Example and others leverages depth information and structural graph-based features to significantly improve segmentation accuracy. Similarly, HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection from the School of Optics and Photonics, Beijing Institute of Technology introduces HSC-SAM, a novel framework that bridges the modality gap by combining spatial and spectral features through a decomposition module, showcasing the power of hyperspectral data.

Another significant area of innovation is robustness to image quality and domain shift. The paper Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement from Northwestern Polytechnical University and Max Planck for Informatics, presents GleSAM++, which enhances SAM’s resilience to low-quality images by integrating generative diffusion models into its latent space. This allows for dynamic denoising based on degradation levels. Complementing this, Towards Integrating Uncertainty for Domain-Agnostic Segmentation by UvA-Bosch Delta Lab, University of Amsterdam, explores uncertainty quantification to improve robustness and trustworthiness in challenging domains, suggesting that a last-layer Laplace approximation can powerfully signal potential segmentation errors. Furthermore, Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios from Tsinghua University introduces VNS-SAM, demonstrating that fine-tuning can significantly improve generalization in visually non-salient contexts, opening new real-world applications.

In the specialized realm of medical imaging, the emphasis is on efficiency, precision, and interpretability. SAM-aware Test-time Adaptation for Universal Medical Image Segmentation by Jianghao Wu showcases a test-time adaptation (TTA) framework that significantly boosts SAM’s performance across diverse medical tasks. Building on this, OFL-SAM2: Prompt SAM2 with Online Few-shot Learner for Efficient Medical Image Segmentation from The Hong Kong University of Science and Technology introduces OFL-SAM2, a prompt-free framework using online few-shot learning and an adaptive fusion module for efficient, accurate segmentation with limited data. Even more ambitious is Bridging the Perception-Cognition Gap: Re-engineering SAM2 with Hilbert-Mamba for Robust VLM-based Medical Diagnosis, which proposes integrating Hilbert-Mamba architecture with SAM2 to improve diagnostic accuracy and interpretability in medical Vision-Language Models (VLMs).

Beyond perception, new work is also refining SAM’s capabilities for structured and dynamic tasks. TopoLoRA-SAM: Topology-Aware Parameter-Efficient Adaptation of Foundation Segmenters for Thin-Structure and Cross-Domain Binary Semantic Segmentation by Salim Khazem (Talan, France) introduces a topology-aware, parameter-efficient adaptation that uses LoRA and specialized losses to preserve thin structures and connectivity, crucial for tasks like retinal vasculature segmentation. For visual object tracking, Rethinking Memory Design in SAM-Based Visual Object Tracking from Khalifa University proposes a unified hybrid memory framework to address limitations in SAM’s memory mechanisms, improving robustness in complex tracking scenarios. Finally, to make full-scene segmentation practical for resource-constrained environments, Tiny-YOLOSAM: Fast Hybrid Image Segmentation by Kenneth Xu and Songhan Wu from the University of Michigan, combines YOLOv12 with TinySAM for fast hybrid segmentation, dramatically reducing runtime while improving coverage.

Under the Hood: Models, Datasets, & Benchmarks

These innovations are powered by novel architectural designs, specialized datasets, and rigorous benchmarks:

• HSC-SAM Framework & HyperCOD Benchmark: Introduced by the School of Optics and Photonics, Beijing Institute of Technology, HSC-SAM adapts SAM to hyperspectral data using spectral-spatial decomposition and saliency-guided token filtering. This work also presents HyperCOD (https://github.com/Baishuyanyan/HyperCOD), the first large-scale benchmark for hyperspectral camouflaged object detection.
• DGA-Net: An enhanced SAM variant for camouflaged object detection, integrating depth prompting and graph-anchor guidance.
• TopoLoRA-SAM: Combines LoRA (Low-Rank Adaptation) and a lightweight spatial adapter with a topology-aware loss (clDice) for parameter-efficient, accurate thin-structure segmentation. Code available at https://github.com/salimkhazem/Seglab.git.
• GleSAM++ Framework & LQSeg Dataset: Northwestern Polytechnical University and Max Planck for Informatics developed GleSAM++, which incorporates generative diffusion models in SAM’s latent space. They also built the LQSeg dataset for diverse image degradation types, promoting robust image analysis. Code and resources at https://guangqian-guo.github.io/glesam++.
• VNS-SAM: A modified SAM enhancing generalization for visually non-salient tasks, with resources available at https://guangqian-guo.github.io/VNS-SAM/.
• SAR SAM Adaptation: Politecnico di Milano, NORCE Norwegian Research Centre AS, and UiT The Arctic University of Norway adapted SAM for SAR remote sensing, utilizing a multi-encoder architecture and tailored prompt strategies for avalanche segmentation.
• SAM-aware Test-Time Adaptation (TTA): A framework for medical image segmentation, improving SAM’s performance by adapting pre-trained models at test time. Implementation available at https://github.com/JianghaoWu/SAM-TTA.
• OFL-SAM2 & Adaptive Fusion Module (AFM): A prompt-free SAM2 framework from The Hong Kong University of Science and Technology for label-efficient medical image segmentation, featuring an online few-shot learner and AFM. Code at https://github.com/xmed-lab/OFL-SAM2.
• Hilbert-Mamba integrated SAM2: Proposed for VLM-based medical diagnosis to bridge the perception-cognition gap, enhancing robustness and interpretability.
• UncertSAM Benchmark: A multi-domain benchmark and a systematic comparison of lightweight, post-hoc uncertainty estimation methods for SAM, provided by UvA-Bosch Delta Lab, University of Amsterdam. Resources at https://github.com/JesseBrouw/UncertSAM.
• SOFTooth: A method for tooth instance segmentation that leverages semantics and order-aware fusion for improved accuracy in dental imaging.
• Unified Hybrid Memory Framework: Designed by Khalifa University for SAM-based visual object tracking, separating short-term and long-term memory for improved robustness. Code at https://github.com/HamadYA/SAM3_Tracking_Zoo.
• Tiny-YOLOSAM: A fast hybrid image segmentation approach combining YOLOv12 with TinySAM for efficient full-scene segmentation. Code available at https://github.com/Kenneth-Xu11566/tiny-yolosam.

Impact & The Road Ahead

These advancements represent a significant leap forward in making SAM, and foundation models in general, more practical, robust, and performant across a wider spectrum of real-world applications. From enhancing medical diagnoses with more reliable segmentation and interpretable VLMs to enabling rapid, accurate environmental monitoring with SAR data for avalanche detection, the implications are vast.

The emphasis on parameter-efficient fine-tuning, domain adaptation, and handling data quality variations suggests a future where foundation models are not just powerful, but also agile and resource-conscious. The development of specialized benchmarks like HyperCOD and LQSeg will fuel further innovation, pushing models to excel in challenging, previously underserved domains. The exploration of uncertainty quantification points towards more trustworthy AI systems, crucial for high-stakes applications like healthcare.

As we move forward, the challenge will be to further unify these specialized adaptations, creating an even more versatile “segment anything and anywhere” model that can seamlessly transition between modalities, quality levels, and semantic complexities. The journey to truly universal and robust perception continues, driven by the ingenuity showcased in these groundbreaking papers. The future of AI-powered vision looks incredibly bright!