EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection

• URL: http://arxiv.org/abs/2602.16238v1
• Date: Wed, 18 Feb 2026 07:28:09 GMT
• Title: EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection
• Authors: Hiroki Nakamura, Hiroto Iino, Masashi Okada, Tadahiro Taniguchi,
• Abstract summary: We propose EasyControlEdge, adapting an image-generation foundation model to edge detection.<n>We show consistent gains under no-post-processing crispness evaluation and with limited training data.
• Score: 9.817969756082995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose EasyControlEdge, adapting an image-generation foundation model to edge detection. In real-world edge detection (e.g., floor-plan walls, satellite roads/buildings, and medical organ boundaries), crispness and data efficiency are crucial, yet producing crisp raw edge maps with limited training samples remains challenging. Although image-generation foundation models perform well on many downstream tasks, their pretrained priors for data-efficient transfer and iterative refinement for high-frequency detail preservation remain underexploited for edge detection. To enable crisp and data-efficient edge detection using these capabilities, we introduce an edge-specialized adaptation of image-generation foundation models. To better specialize the foundation model for edge detection, we incorporate an edge-oriented objective with an efficient pixel-space loss. At inference, we introduce guidance based on unconditional dynamics, enabling a single model to control the edge density through a guidance scale. Experiments on BSDS500, NYUDv2, BIPED, and CubiCasa compare against state-of-the-art methods and show consistent gains, particularly under no-post-processing crispness evaluation and with limited training data.

Related papers

• Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment [59.61554561979589]
  Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios.<n>Existing edge detection methods face challenges: difficulty balancing detection precision with lightweight models, limited adaptability, and insufficient real-world validation.<n>We propose the Edge Detection Toolbox (ED-TOOLBOX), which utilizes generalizable plug-and-play components to adapt object detection models for edge environments.
  arXiv  Detail & Related papers  (2024-12-24T07:28:10Z)
• Generative Edge Detection with Stable Diffusion [52.870631376660924]
  Edge detection is typically viewed as a pixel-level classification problem mainly addressed by discriminative methods. We propose a novel approach, named Generative Edge Detector (GED), by fully utilizing the potential of the pre-trained stable diffusion model. We conduct extensive experiments on multiple datasets and achieve competitive performance.
  arXiv  Detail & Related papers  (2024-10-04T01:52:23Z)
• Cycle Pixel Difference Network for Crisp Edge Detection [14.625034156501778]
  Edge detection is a fundamental task in computer vision.<n>Recent deep learning-based methods face two significant issues: 1) reliance on large-scale pre-trained weights, and 2) generation of thick edges.<n>We construct a U-shape encoder-decoder model named CPD-Net that successfully addresses these two issues simultaneously.
  arXiv  Detail & Related papers  (2024-09-06T13:28:05Z)
• Learning to utilize image second-order derivative information for crisp edge detection [16.152236524867078]
  Edge detection is a fundamental task in computer vision.<n>Recent top-performing edge detection methods tend to generate thick and noisy edge lines.<n>We propose a second-order derivative-based multi-scale contextual enhancement module (SDMCM) to help the model locate true edge pixels accurately.<n>We also construct a hybrid focal loss function (HFL) to alleviate the imbalanced distribution issue.<n>In the end, we propose a U-shape network named LUS-Net which is based on the SDMCM and BRM for edge detection.
  arXiv  Detail & Related papers  (2024-06-09T13:25:02Z)
• SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection [2.912976132828368]
  State-of-the-art pixel-wise annotations are labor-intensive and subject to inconsistencies when acquired manually. We propose a novel self-supervised approach for edge detection that employs a multi-level, multi-homography technique to transfer annotations from synthetic to real-world datasets. Our method eliminates the dependency on manual annotated edge labels, thereby enhancing its generalizability across diverse datasets.
  arXiv  Detail & Related papers  (2024-01-04T15:21:53Z)
• DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection [20.278655159290302]
  We propose the first diffusion model for the task of general edge detection, which we call DiffusionEdge. To avoid expensive computational resources while retaining the final performance, we apply DPM in the latent space and enable the classic cross-entropy loss. With all the technical designs, DiffusionEdge can be stably trained with limited resources, predicting crisp and accurate edge maps with much fewer augmentation strategies.
  arXiv  Detail & Related papers  (2024-01-04T02:20:54Z)
• Parallax-Tolerant Unsupervised Deep Image Stitching [57.76737888499145]
  We propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique. First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion. To further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks.
  arXiv  Detail & Related papers  (2023-02-16T10:40:55Z)
• Texture-guided Saliency Distilling for Unsupervised Salient Object Detection [67.10779270290305]
  We propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. Our method achieves state-of-the-art USOD performance on RGB, RGB-D, RGB-T, and video SOD benchmarks.
  arXiv  Detail & Related papers  (2022-07-13T02:01:07Z)
• STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization [15.579360385857129]
  We study the problem of self-training edge detection, leveraging the untapped wealth of large-scale unlabeled image datasets. We design a self-supervised framework with multi-layer regularization and self-teaching. Our method attains 4.8% improvement for ODS and 5.8% for OIS when tested on the unseen BIPED dataset.
  arXiv  Detail & Related papers  (2022-01-13T18:26:36Z)
• Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection [63.942632088208505]
  We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
  arXiv  Detail & Related papers  (2020-03-28T18:51:43Z)
• DeepStrip: High Resolution Boundary Refinement [60.00241966809684]
  We propose to convert regions of interest into strip images and compute a boundary prediction in the strip domain. To detect the target boundary, we present a framework with two prediction layers. We enforce a matching consistency and C0 continuity regularization to the network to reduce false alarms.
  arXiv  Detail & Related papers  (2020-03-25T22:44:48Z)

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.