Zero-Shot Edge Detection with SCESAME: Spectral Clustering-based
Ensemble for Segment Anything Model Estimation
- URL: http://arxiv.org/abs/2308.13779v2
- Date: Sat, 18 Nov 2023 12:41:22 GMT
- Title: Zero-Shot Edge Detection with SCESAME: Spectral Clustering-based
Ensemble for Segment Anything Model Estimation
- Authors: Hiroaki Yamagiwa, Yusuke Takase, Hiroyuki Kambe, Ryosuke Nakamoto
- Abstract summary: This paper proposes a novel zero-shot edge detection with SCESAME, based on the recently proposed Segment Anything Model (SAM)
AMG can be applied to edge detection, but suffers from the problem of overdetecting edges.
We performed edge detection experiments on two datasets, BSDS500 and NYUDv2.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper proposes a novel zero-shot edge detection with SCESAME, which
stands for Spectral Clustering-based Ensemble for Segment Anything Model
Estimation, based on the recently proposed Segment Anything Model (SAM). SAM is
a foundation model for segmentation tasks, and one of the interesting
applications of SAM is Automatic Mask Generation (AMG), which generates
zero-shot segmentation masks of an entire image. AMG can be applied to edge
detection, but suffers from the problem of overdetecting edges. Edge detection
with SCESAME overcomes this problem by three steps: (1) eliminating small
generated masks, (2) combining masks by spectral clustering, taking into
account mask positions and overlaps, and (3) removing artifacts after edge
detection. We performed edge detection experiments on two datasets, BSDS500 and
NYUDv2. Although our zero-shot approach is simple, the experimental results on
BSDS500 showed almost identical performance to human performance and CNN-based
methods from seven years ago. In the NYUDv2 experiments, it performed almost as
well as recent CNN-based methods. These results indicate that our method
effectively enhances the utility of SAM and can be a new direction in zero-shot
edge detection methods.
Related papers
- From Generalization to Precision: Exploring SAM for Tool Segmentation in
Surgical Environments [7.01085327371458]
We argue that Segment Anything Model drastically over-segment images with high corruption levels, resulting in degraded performance.
We employ the ground-truth tool mask to analyze the results of SAM when the best single mask is selected as prediction.
We analyze the Endovis18 and Endovis17 instrument segmentation datasets using synthetic corruptions of various strengths and an In-House dataset featuring counterfactually created real-world corruptions.
arXiv Detail & Related papers (2024-02-28T01:33:49Z) - SAM-based instance segmentation models for the automation of structural
damage detection [0.0]
We present a data set for instance segmentation with 1,300 annotated images (640 pixels x 640 pixels), named as M1300, covering bricks, broken bricks, and cracks.
We test several leading algorithms for benchmarking, including the latest large-scale model, the prompt-based Segment Anything Model (SAM)
We propose two novel methods for automation of SAM execution. The first method involves abandoning the prompt encoder and connecting the SAM encoder to other decoders, while the second method introduces a learnable self-generating prompter.
arXiv Detail & Related papers (2024-01-27T02:00:07Z) - UGainS: Uncertainty Guided Anomaly Instance Segmentation [80.12253291709673]
A single unexpected object on the road can cause an accident or lead to injuries.
Current approaches tackle anomaly segmentation by assigning an anomaly score to each pixel.
We propose an approach that produces accurate anomaly instance masks.
arXiv Detail & Related papers (2023-08-03T20:55:37Z) - Monte Carlo Linear Clustering with Single-Point Supervision is Enough
for Infrared Small Target Detection [48.707233614642796]
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images.
Deep learning based methods have achieved promising performance on SIRST detection, but at the cost of a large amount of training data.
We propose the first method to achieve SIRST detection with single-point supervision.
arXiv Detail & Related papers (2023-04-10T08:04:05Z) - Cut and Learn for Unsupervised Object Detection and Instance
Segmentation [65.43627672225624]
Cut-and-LEaRn (CutLER) is a simple approach for training unsupervised object detection and segmentation models.
CutLER is a zero-shot unsupervised detector and improves detection performance AP50 by over 2.7 times on 11 benchmarks.
arXiv Detail & Related papers (2023-01-26T18:57:13Z) - DAAIN: Detection of Anomalous and Adversarial Input using Normalizing
Flows [52.31831255787147]
We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA)
Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution.
Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
arXiv Detail & Related papers (2021-05-30T22:07:13Z) - Object Detection Made Simpler by Eliminating Heuristic NMS [70.93004137521946]
We show a simple NMS-free, end-to-end object detection framework.
We attain on par or even improved detection accuracy compared with the original one-stage detector.
arXiv Detail & Related papers (2021-01-28T02:38:29Z) - Weakly-Supervised Saliency Detection via Salient Object Subitizing [57.17613373230722]
We introduce saliency subitizing as the weak supervision since it is class-agnostic.
This allows the supervision to be aligned with the property of saliency detection.
We conduct extensive experiments on five benchmark datasets.
arXiv Detail & Related papers (2021-01-04T12:51:45Z) - CAFENet: Class-Agnostic Few-Shot Edge Detection Network [19.01453512012934]
We tackle a novel few-shot learning challenge, which we call few-shot semantic edge detection.
We also present a Class-Agnostic Few-shot Edge detection Network (CAFENet) based on meta-learning strategy.
arXiv Detail & Related papers (2020-03-18T14:18:59Z)
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.