NubbleDrop: A Simple Way to Improve Matching Strategy for Prompted One-Shot Segmentation

• URL: http://arxiv.org/abs/2405.11476v1
• Date: Sun, 19 May 2024 08:00:38 GMT
• Title: NubbleDrop: A Simple Way to Improve Matching Strategy for Prompted One-Shot Segmentation
• Authors: Zhiyu Xu, Qingliang Chen,
• Abstract summary: We propose a simple and training-free method to enhance the validity and robustness of the matching strategy. The core concept involves randomly dropping feature channels (setting them to zero) during the matching process. This technique mimics discarding pathological nubbles, and it can be seamlessly applied to other similarity computing scenarios.
• Score: 2.2559617939136505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driven by large data trained segmentation models, such as SAM , research in one-shot segmentation has experienced significant advancements. Recent contributions like PerSAM and MATCHER , presented at ICLR 2024, utilize a similar approach by leveraging SAM with one or a few reference images to generate high quality segmentation masks for target images. Specifically, they utilize raw encoded features to compute cosine similarity between patches within reference and target images along the channel dimension, effectively generating prompt points or boxes for the target images a technique referred to as the matching strategy. However, relying solely on raw features might introduce biases and lack robustness for such a complex task. To address this concern, we delve into the issues of feature interaction and uneven distribution inherent in raw feature based matching. In this paper, we propose a simple and training-free method to enhance the validity and robustness of the matching strategy at no additional computational cost (NubbleDrop). The core concept involves randomly dropping feature channels (setting them to zero) during the matching process, thereby preventing models from being influenced by channels containing deceptive information. This technique mimics discarding pathological nubbles, and it can be seamlessly applied to other similarity computing scenarios. We conduct a comprehensive set of experiments, considering a wide range of factors, to demonstrate the effectiveness and validity of our proposed method. Our results showcase the significant improvements achieved through this simmple and straightforward approach.

Related papers

• One Shot is Enough for Sequential Infrared Small Target Segmentation [9.354927663020586]
  Infrared small target sequences exhibit strong similarities between frames and contain rich contextual information. We propose a one-shot and training-free method that perfectly adapts SAM's zero-shot generalization capability to sequential IRSTS. Experiments demonstrate that our method requires only one shot to achieve comparable performance to state-of-the-art IRSTS methods.
  arXiv  Detail & Related papers  (2024-08-09T02:36:56Z)
• Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention [7.939095881813804]
  Few-shot segmentation aims to train a segmentation model that can fast adapt to a novel task for which only a few annotated images are provided. We introduce an instance-aware data augmentation (IDA) strategy that augments the support images based on the relative sizes of the target objects. The proposed IDA effectively increases the support set's diversity and promotes the distribution consistency between support and query images.
  arXiv  Detail & Related papers  (2024-01-18T10:29:10Z)
• Multilayer Multiset Neuronal Networks -- MMNNs [55.2480439325792]
  The present work describes multilayer multiset neuronal networks incorporating two or more layers of coincidence similarity neurons. The work also explores the utilization of counter-prototype points, which are assigned to the image regions to be avoided.
  arXiv  Detail & Related papers  (2023-08-28T12:55:13Z)
• Learning Image Deraining Transformer Network with Dynamic Dual Self-Attention [46.11162082219387]
  This paper proposes an effective image deraining Transformer with dynamic dual self-attention (DDSA) Specifically, we only select the most useful similarity values based on top-k approximate calculation to achieve sparse attention. In addition, we also develop a novel spatial-enhanced feed-forward network (SEFN) to further obtain a more accurate representation for achieving high-quality derained results.
  arXiv  Detail & Related papers  (2023-08-15T13:59:47Z)
• CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation [73.89509052503222]
  This paper presents a simple but performant semi-supervised semantic segmentation approach, called CorrMatch. We observe that the correlation maps not only enable clustering pixels of the same category easily but also contain good shape information. We propose to conduct pixel propagation by modeling the pairwise similarities of pixels to spread the high-confidence pixels and dig out more. Then, we perform region propagation to enhance the pseudo labels with accurate class-agnostic masks extracted from the correlation maps.
  arXiv  Detail & Related papers  (2023-06-07T10:02:29Z)
• Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization [73.04187954213471]
  We introduce a unified learning approach to simultaneously modeling the coarse- and fine-grained retrieval. The proposed method has achieved +4.03%, +3.38%, and +2.40% Recall@50 accuracy over a strong baseline.
  arXiv  Detail & Related papers  (2022-11-14T14:25:40Z)
• Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation [0.456877715768796]
  We propose a framework for self-supervised image segmentation using contrastive learning on image patches. A fully convolutional neural network (FCNN) is trained in a self-supervised manner to discern features in the input images. The proposed model only consists of a simple FCNN with 10.8k parameters and requires about 5 minutes to converge on the high resolution microscopy datasets.
  arXiv  Detail & Related papers  (2022-08-23T07:24:47Z)
• SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes [56.387647750094466]
  Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples. Most of advanced solutions exploit a metric learning framework that performs segmentation through matching each pixel to a learned foreground prototype. This framework suffers from biased classification due to incomplete construction of sample pairs with the foreground prototype only.
  arXiv  Detail & Related papers  (2021-04-19T11:21:47Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)
• Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification [58.20132466198622]
  We propose Attentive CutMix, a naturally enhanced augmentation strategy based on CutMix. In each training iteration, we choose the most descriptive regions based on the intermediate attention maps from a feature extractor. Our proposed method is simple yet effective, easy to implement and can boost the baseline significantly.
  arXiv  Detail & Related papers  (2020-03-29T15:01:05Z)

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.