Enhanced Extractor-Selector Framework and Symmetrization Weighted Binary Cross-Entropy for Edge Detections

• URL: http://arxiv.org/abs/2501.13365v1
• Date: Thu, 23 Jan 2025 04:10:31 GMT
• Title: Enhanced Extractor-Selector Framework and Symmetrization Weighted Binary Cross-Entropy for Edge Detections
• Authors: Hao Shu,
• Abstract summary: Recent advancements have demonstrated the effectiveness of the extractor-selector (E-S) framework in edge detection (ED) tasks. We propose an enhanced E-S architecture, which utilizes richer, less-loss feature representations. We introduce a novel loss function, the Symmetrization Weight Binary Cross-Entropy (SWBCE), which simultaneously emphasizes both the recall of edge pixels and the suppression of erroneous edge predictions.
• Score: 0.0
• License:
• Abstract: Recent advancements have demonstrated the effectiveness of the extractor-selector (E-S) framework in edge detection (ED) tasks, which achieves state-of-the-art (SOTA) performance in both quantitative metrics and perceptual quality. However, this method still falls short of fully exploiting the potential of feature extractors, as selectors only operate on highly compressed feature maps that lack diversity and suffer from substantial information loss. Additionally, while union training can improve perceptual quality, the highest evaluation scores are typically obtained without it, creating a trade-off between quantitative accuracy and perceptual fidelity. To address these limitations, we propose an enhanced E-S architecture, which utilizes richer, less-loss feature representations and incorporates auxiliary features during the selection process, thereby improving the effectiveness of the feature selection mechanism. Additionally, we introduce a novel loss function, the Symmetrization Weight Binary Cross-Entropy (SWBCE), which simultaneously emphasizes both the recall of edge pixels and the suppression of erroneous edge predictions, thereby enhancing the predictions both in the perceptual quality and the prediction accuracy. The effectiveness and superiority of our approaches over baseline models, the standard E-S framework, and the standard Weight Binary Cross-Entropy (WBCE) loss function are demonstrated by extensive experiments. For example, our enhanced E-S architecture trained with SWBCE loss function achieves average improvements of 8.25$\%$, 8.01$\%$, and 33.25$\%$ in ODS, OIS, and AP, measured on BIPED2 compared with the baseline models, significantly outperforming the standard E-S method. The results set new benchmarks for ED tasks, and highlight the potential of the methods in beyond.

Related papers

• Pixel-Wise Feature Selection for Perceptual Edge Detection without post-processing [0.0]
  This paper proposes a novel feature selection paradigm for deep networks that facilitates the differential selection of features and can be seamlessly integrated into existing ED models. By incorporating this additional structure, the performance of conventional ED models is substantially enhanced without post-processing, while simultaneously enhancing the perceptual quality of the predictions.
  arXiv  Detail & Related papers  (2025-01-05T13:28:37Z)
• UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation [93.38604803625294]
  We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) We use Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. UncertaintyRAG outperforms baselines by 2.03% on LLaMA-2-7B, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-03T17:39:38Z)
• A Simple and Generalist Approach for Panoptic Segmentation [57.94892855772925]
  Generalist vision models aim for one and the same architecture for a variety of vision tasks. While such shared architecture may seem attractive, generalist models tend to be outperformed by their bespoken counterparts. We address this problem by introducing two key contributions, without compromising the desirable properties of generalist models.
  arXiv  Detail & Related papers  (2024-08-29T13:02:12Z)
• Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders [6.7181844004432385]
  The Inter-Intra Modal Measure (IIMM) functions as a strong predictor of performance changes with fine-tuning. Fine-tuning on tasks with higher IIMM scores produces greater in-domain performance gains but also induces more severe out-of-domain performance degradation. With only a single forward pass of the target data, practitioners can leverage this key insight to evaluate the degree to which a model can be expected to improve following fine-tuning.
  arXiv  Detail & Related papers  (2024-07-22T15:35:09Z)
• Uncertainty Quantification for Bird's Eye View Semantic Segmentation: Methods and Benchmarks [10.193504550494486]
  This paper introduces a benchmark for predictive uncertainty quantification in BEV segmentation. It focuses on the effectiveness of predicted uncertainty in identifying misclassified and out-of-distribution pixels, as well as calibration. We propose the Uncertainty-Focal-Cross-Entropy loss, designed for highly imbalanced data, which consistently improves the segmentation quality and calibration.
  arXiv  Detail & Related papers  (2024-05-31T16:32:46Z)
• HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images [6.890268321645873]
  We propose a progressive foreground-balanced sampling strategy on the basis of not adding change information. This strategy helps the model accurately learn the features of the changed pixels during the early training process. We also design a discriminative Siamese network, hierarchical attention network (HANet), which can integrate multiscale features and refine detailed features.
  arXiv  Detail & Related papers  (2024-04-14T08:01:27Z)
• Produce Once, Utilize Twice for Anomaly Detection [6.501323305130114]
  We derive POUTA, which improves both the accuracy and efficiency by reusing the discriminant information potential in the reconstructive network. POUTA achieves better performance than the state-of-the-art few-shot anomaly detection methods without any special design.
  arXiv  Detail & Related papers  (2023-12-20T10:49:49Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification [123.93460670568554]
  This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network. The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
  arXiv  Detail & Related papers  (2021-01-24T19:03:10Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)

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.