Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation

• URL: http://arxiv.org/abs/2312.03502v2
• Date: Wed, 10 Apr 2024 08:29:23 GMT
• Title: Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
• Authors: Haojie Zhang, Yongyi Su, Xun Xu, Kui Jia,
• Abstract summary: 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.
• Score: 43.759808066264334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prompt engineering. Segment-Anything(SAM), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot generalization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. In particular, SAM performs awkwardly on corrupted natural images, camouflaged images, medical images, etc. Motivated by the observations, we aim to develop a self-training based strategy to adapt SAM to target distribution. Given the unique challenges of large source dataset, high computation cost and incorrect pseudo label, we propose a weakly supervised self-training architecture with anchor regularization and low-rank finetuning to improve the robustness and computation 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. Our proposed method is task-agnostic in nature and outperforms pre-trained SAM and state-of-the-art domain adaptation methods on almost all downstream tasks with the same testing prompt inputs.

Related papers

• 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)
• RobustSAM: Segment Anything Robustly on Degraded Images [19.767828436963317]
  Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation. We propose the Robust Segment Anything Model (RobustSAM), which enhances SAM's performance on low-quality images. Our method has been shown to effectively improve the performance of SAM-based downstream tasks such as single image dehazing and deblurring.
  arXiv  Detail & Related papers  (2024-06-13T23:33:59Z)
• 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)
• 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)
• Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study [61.65123150513683]
  multimodal foundation models, such as CLIP, produce state-of-the-art zero-shot results. It is reported that these models close the robustness gap by matching the performance of supervised models trained on ImageNet. We show that CLIP leads to a significant robustness drop compared to supervised ImageNet models on our benchmark.
  arXiv  Detail & Related papers  (2024-03-15T17:33:49Z)
• 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)
• On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation [47.95611203419802]
  Foundations for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. We compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset. We further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution data.
  arXiv  Detail & Related papers  (2023-11-18T14:52:10Z)
• Black-box Targeted Adversarial Attack on Segment Anything (SAM) [24.927514923402775]
  This work aims to achieve a targeted adversarial attack (TAA) on Segment Anything Model (SAM) Specifically, under a certain prompt, the goal is to make the predicted mask of an adversarial example resemble that of a given target image. We propose a novel regularization loss to enhance the cross-model transferability by increasing the feature dominance of adversarial images over random natural images.
  arXiv  Detail & Related papers  (2023-10-16T02:09:03Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)

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.