ASAM: Boosting Segment Anything Model with Adversarial Tuning

• URL: http://arxiv.org/abs/2405.00256v1
• Date: Wed, 1 May 2024 00:13:05 GMT
• Title: ASAM: Boosting Segment Anything Model with Adversarial Tuning
• Authors: Bo Li, Haoke Xiao, Lv Tang,
• Abstract summary: This paper introduces ASAM, a novel methodology that amplifies a foundation model's performance through adversarial tuning. We harness the potential of natural adversarial examples, inspired by their successful implementation in natural language processing. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations.
• Score: 9.566046692165884
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the evolving landscape of computer vision, foundation models have emerged as pivotal tools, exhibiting exceptional adaptability to a myriad of tasks. Among these, the Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmentation. However, SAM, like its counterparts, encounters limitations in specific niche applications, prompting a quest for enhancement strategies that do not compromise its inherent capabilities. This paper introduces ASAM, a novel methodology that amplifies SAM's performance through adversarial tuning. We harness the potential of natural adversarial examples, inspired by their successful implementation in natural language processing. By utilizing a stable diffusion model, we augment a subset (1%) of the SA-1B dataset, generating adversarial instances that are more representative of natural variations rather than conventional imperceptible perturbations. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations, thereby preserving the integrity of the segmentation task. The fine-tuned ASAM demonstrates significant improvements across a diverse range of segmentation tasks without necessitating additional data or architectural modifications. The results of our extensive evaluations confirm that ASAM establishes new benchmarks in segmentation tasks, thereby contributing to the advancement of foundational models in computer vision. Our project page is in https://asam2024.github.io/.

Related papers

• Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning [63.55145330447408]
  Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. Existing methods that directly apply SAM through prompting often overlook the domain shift issue. We propose a novel Self-Perceptinon Tuning (SPT) method, aiming to enhance SAM's perception capability for anomaly segmentation.
  arXiv  Detail & Related papers  (2024-11-26T08:33:25Z)
• On Efficient Variants of Segment Anything Model: A Survey [63.127753705046]
  The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. To address this, a variety of SAM variants have been proposed to enhance efficiency while keeping accuracy. This survey provides the first comprehensive review of these efficient SAM variants.
  arXiv  Detail & Related papers  (2024-10-07T11:59:54Z)
• Adapting Segment Anything Model for Unseen Object Instance Segmentation [70.60171342436092]
  Unseen Object Instance (UOIS) is crucial for autonomous robots operating in unstructured environments. We propose UOIS-SAM, a data-efficient solution for the UOIS task. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder.
  arXiv  Detail & Related papers  (2024-09-23T19:05:50Z)
• SAM-SP: Self-Prompting Makes SAM Great Again [11.109389094334894]
  Segment Anything Model (SAM) has demonstrated impressive capabilities in zero-shot segmentation tasks. SAM encounters noticeably degradation performance when applied to specific domains, such as medical images. We introduce a novel self-prompting based fine-tuning approach, called SAM-SP, tailored for extending the vanilla SAM model.
  arXiv  Detail & Related papers  (2024-08-22T13:03:05Z)
• AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning [61.666973416903005]
  Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prompts. We propose a novel framework, termed AlignSAM, designed for automatic prompting for aligning SAM to an open context.
  arXiv  Detail & Related papers  (2024-06-01T16:21:39Z)
• SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes [34.796859088106636]
  Segment anything model (SAM) has demonstrated excellent generalizability in common vision scenarios, yet falling short of the ability to understand specialized data. Recent methods have combined parameter-efficient techniques with task-specific designs to fine-tune SAM on particular tasks. We present a simple and unified framework, namely SU-SAM, that can easily and efficiently fine-tune the SAM model with parameter-efficient techniques.
  arXiv  Detail & Related papers  (2024-01-31T12:53:11Z)
• Boosting Segment Anything Model Towards Open-Vocabulary Learning [69.42565443181017]
  Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model. Despite SAM finding applications and adaptations in various domains, its primary limitation lies in the inability to grasp object semantics. We present Sambor to seamlessly integrate SAM with the open-vocabulary object detector in an end-to-end framework.
  arXiv  Detail & Related papers  (2023-12-06T17:19:00Z)
• Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation [43.759808066264334]
  We propose a weakly supervised self-training architecture with anchor regularization and low-rank finetuning to improve the robustness and efficiency of adaptation. We validate the effectiveness on 5 types of downstream segmentation tasks including natural clean/corrupted images, medical images, camouflaged images and robotic images.
  arXiv  Detail & Related papers  (2023-12-06T13:59:22Z)
• Stable Segment Anything Model [79.9005670886038]
  The Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts. This paper presents the first comprehensive analysis on SAM's segmentation stability across a diverse spectrum of prompt qualities. Our solution, termed Stable-SAM, offers several advantages: 1) improved SAM's segmentation stability across a wide range of prompt qualities, while 2) retaining SAM's powerful promptable segmentation efficiency and generality.
  arXiv  Detail & Related papers  (2023-11-27T12:51:42Z)
• Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM) [8.529233820032678]
  The Segment Anything Model (SAM) is the first foundation model for image segmentation. In this study, we evaluate SAM's ability to segment features from eye images recorded in virtual reality setups. Our investigation centers on SAM's zero-shot learning abilities and the effectiveness of prompts like bounding boxes or point clicks.
  arXiv  Detail & Related papers  (2023-11-14T11:05:08Z)

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.