Don't waste SAM

• URL: http://arxiv.org/abs/2606.10696v1
• Date: Tue, 09 Jun 2026 10:53:33 GMT
• Title: Don't waste SAM
• Abstract summary: The fine-tuned SAM-ViT-H model outperforms the state-ofthe-art Zerowaste, and TACO datasets with a significant increase of +30 in IoU.<n>It closely approaches performance levels of TrashCan 1.0, with only a -1.44 difference.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Meta AI has recently released the Segment Anything Model (SAM), which demonstrates exceptional zero-shot image segmentation performance across various tasks with remarkable accuracy. Despite its inability to provide accurate segmentation across multiple research fields, SAM still serves as a valuable starting point for supporting the segmentation pipeline process, particularly for tasks that require extensive and senior skills annotations. This study aims to evaluate the generalization of SAM and fine-tuning SAM models using three waste segmentation datasets. Although they are captured from real scenes as SAM was pretrained on, these datasets present several challenges, including occlusions, deformable objects, transparency, and objects easily confused with backgrounds. In our findings, the fine-tuned SAM-ViT-H model outperforms the state-ofthe-art Zerowaste, and TACO datasets with a significant increase of +30 in IoU, and it closely approaches performance levels of TrashCan 1.0, with only a -1.44 difference. After evaluating these popular waste datasets, it became evident that fine-tuning SAM as a foundational model is a crucial step for providing better generalization for downstream waste segmentation tasks. Therefore, SAM should not be disregarded or wasted.

Related papers

• Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios [31.56671125284146]
  We propose Visually Non-Salient SAM (VNS-SAM) for visually non-salient scenarios.<n>VNS-SAM aims to enhance SAM's perception of visually non-salient scenarios while preserving its original zero-shot generalizability.<n>Designs help the SAM decoder gain a deeper understanding of non-salient characteristics with only marginal parameter increments.
  arXiv  Detail & Related papers  (2026-01-02T02:42:04Z)
• 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)
• Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection [58.241593208031816]
  Segment Anything Model (SAM) has been proposed as a visual fundamental model, which gives strong segmentation and generalization capabilities. We propose a Multi-scale and Detail-enhanced SAM (MDSAM) for Salient Object Detection (SOD) Experimental results demonstrate the superior performance of our model on multiple SOD datasets.
  arXiv  Detail & Related papers  (2024-08-08T09:09:37Z)
• Segment Anything without Supervision [65.93211374889196]
  We present Unsupervised SAM (UnSAM) for promptable and automatic whole-image segmentation. UnSAM utilizes a divide-and-conquer strategy to "discover" the hierarchical structure of visual scenes. We show that supervised SAM can also benefit from our self-supervised labels.
  arXiv  Detail & Related papers  (2024-06-28T17:47:32Z)
• ASAM: Boosting Segment Anything Model with Adversarial Tuning [9.566046692165884]
  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.
  arXiv  Detail & Related papers  (2024-05-01T00:13:05Z)
• BLO-SAM: Bi-level Optimization Based Overfitting-Preventing Finetuning of SAM [37.1263294647351]
  We introduce BLO-SAM, which finetunes the Segment Anything Model (SAM) based on bi-level optimization (BLO) BLO-SAM reduces the risk of overfitting by training the model's weight parameters and the prompt embedding on two separate subsets of the training dataset. Results demonstrate BLO-SAM's superior performance over various state-of-the-art image semantic segmentation methods.
  arXiv  Detail & Related papers  (2024-02-26T06:36:32Z)
• 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)
• Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation [11.133337712285424]
  We propose DIS-SAM, which advances the Segment Anything Model (SAM) towards highly accurate object segmentation.<n> DIS-SAM employs a two-stage approach, integrating SAM with a modified advanced network that was previously designed to handle the prompt-free DIS task.<n>Despite its simplicity, DIS-SAM significantly advances the SAM, HQ-SAM, and Pi-SAM by 8.5%, 6.9%, and 3.7% maximum F-measure.
  arXiv  Detail & Related papers  (2023-12-30T14:24:33Z)
• TinySAM: Pushing the Envelope for Efficient Segment Anything Model [73.06322749886483]
  We propose a framework to obtain a tiny segment anything model (TinySAM) while maintaining the strong zero-shot performance.<n>With all these proposed methods, our TinySAM leads to orders of magnitude computational reduction and pushes the envelope for efficient segment anything task.
  arXiv  Detail & Related papers  (2023-12-21T12:26:11Z)
• 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)
• On the Robustness of Segment Anything [46.669794757467166]
  We aim to study the testing-time robustness of SAM under adversarial scenarios and common corruptions. We find that SAM exhibits remarkable robustness against various corruptions, except for blur-related corruption.
  arXiv  Detail & Related papers  (2023-05-25T16:28:30Z)
• SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model [85.85899655118087]
  We develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude.
  arXiv  Detail & Related papers  (2023-05-03T10:58:07Z)

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.