AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model

• URL: http://arxiv.org/abs/2410.09714v1
• Date: Sun, 13 Oct 2024 03:47:20 GMT
• Title: AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model
• Authors: Yuchen Li, Li Zhang, Youwei Liang, Pengtao Xie,
• Abstract summary: We propose an automated prompting and mask calibration method called AM-SAM. Our approach automatically generates prompts for an input image, eliminating the need for human involvement with a good performance in early training epochs. Our experimental results demonstrate that AM-SAM achieves significantly accurate segmentation, matching or exceeding the effectiveness of human-generated and default prompts.
• Score: 28.343378406337077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segment Anything Model (SAM) has gained significant recognition in the field of semantic segmentation due to its versatile capabilities and impressive performance. Despite its success, SAM faces two primary limitations: (1) it relies heavily on meticulous human-provided prompts like key points, bounding boxes or text messages, which is labor-intensive; (2) the mask decoder's feature representation is sometimes inaccurate, as it solely employs dot product operations at the end of mask decoder, which inadequately captures the necessary correlations for precise segmentation. Current solutions to these problems such as fine-tuning SAM often require retraining a large number of parameters, which needs huge amount of time and computing resources. To address these limitations, we propose an automated prompting and mask calibration method called AM-SAM based on a bi-level optimization framework. Our approach automatically generates prompts for an input image, eliminating the need for human involvement with a good performance in early training epochs, achieving faster convergence. Additionally, we freeze the main part of SAM, and modify the mask decoder with Low-Rank Adaptation (LoRA), enhancing the mask decoder's feature representation by incorporating advanced techniques that go beyond simple dot product operations to more accurately capture and utilize feature correlations. Our experimental results demonstrate that AM-SAM achieves significantly accurate segmentation, matching or exceeding the effectiveness of human-generated and default prompts. Notably, on the body segmentation dataset, our method yields a 5% higher dice score with a 4-example few-shot training set compared to the SOTA method, underscoring its superiority in semantic segmentation tasks.

Related papers

• Bridge the Points: Graph-based Few-shot Segment Anything Semantically [79.1519244940518]
  Recent advancements in pre-training techniques have enhanced the capabilities of vision foundation models. Recent studies extend the SAM to Few-shot Semantic segmentation (FSS) We propose a simple yet effective approach based on graph analysis.
  arXiv  Detail & Related papers  (2024-10-09T15:02:28Z)
• 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)
• FocSAM: Delving Deeply into Focused Objects in Segmenting Anything [58.042354516491024]
  The Segment Anything Model (SAM) marks a notable milestone in segmentation models. We propose FocSAM with a pipeline redesigned on two pivotal aspects. First, we propose Dynamic Window Multi-head Self-Attention (Dwin-MSA) to dynamically refocus SAM's image embeddings on the target object. Second, we propose Pixel-wise Dynamic ReLU (P-DyReLU) to enable sufficient integration of interactive information from a few initial clicks.
  arXiv  Detail & Related papers  (2024-05-29T02:34:13Z)
• Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding [15.401507589312702]
  This paper introduces H-SAM, a prompt-free adaptation of the Segment Anything Model (SAM) for efficient fine-tuning of medical images. In the initial stage, H-SAM employs SAM's original decoder to generate a prior probabilistic mask, guiding a more intricate decoding process. Our H-SAM demonstrates a 4.78% improvement in average Dice compared to existing prompt-free SAM variants.
  arXiv  Detail & Related papers  (2024-03-27T05:55:16Z)
• 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)
• WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images [8.179859593451285]
  We present WSI-SAM, enhancing Segment Anything Model (SAM) with precise object segmentation capabilities for histopathology images. To fully exploit pretrained knowledge while minimizing training overhead, we keep SAM frozen, introducing only minimal extra parameters. Our model outperforms SAM by 4.1 and 2.5 percent points on a ductal carcinoma in situ (DCIS) segmentation tasks and breast cancer metastasis segmentation task.
  arXiv  Detail & Related papers  (2024-03-14T10:30:43Z)
• Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM [55.93697196726016]
  We propose a simple yet effective crowd counting method by utilizing the Segment-Everything-Everywhere Model (SEEM) We show that SEEM's performance in dense crowd scenes is limited, primarily due to the omission of many persons in high-density areas. Our proposed method achieves the best unsupervised performance in crowd counting, while also being comparable to some supervised methods.
  arXiv  Detail & Related papers  (2024-02-27T13:55:17Z)
• 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)
• EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything [36.553867358541154]
  Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. We propose EfficientSAMs, light-weight SAM models that exhibits decent performance with largely reduced complexity. Our idea is based on leveraging masked image pretraining, SAMI, which learns to reconstruct features from SAM image encoder for effective visual representation learning.
  arXiv  Detail & Related papers  (2023-12-01T18:31:00Z)
• 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)

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.