Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking

• URL: http://arxiv.org/abs/2502.17766v1
• Date: Tue, 25 Feb 2025 01:54:40 GMT
• Title: Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking
• Abstract summary: RANK-LETR is a novel Transformer-based line segment detection method.<n>We propose a new line segment proposal method, wherein the feature point nearest to the centroid of the line segment directly predicts the location.<n> Experimental results demonstrate that our method outperforms other Transformer-based and CNN-based approaches in prediction accuracy.
• Score: 21.2588921381058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confidence scores during prediction, causing them to be ranked lower and potentially suppressed. Additionally, these models often require prolonged training periods to achieve strong performance, largely due to the necessity of bipartite matching. In this paper, we introduce RANK-LETR, a novel Transformer-based line segment detection method. Our approach leverages learnable geometric information to refine the ranking of predicted line segments by enhancing the confidence scores of high-quality predictions in a posterior verification step. We also propose a new line segment proposal method, wherein the feature point nearest to the centroid of the line segment directly predicts the location, significantly improving training efficiency and stability. Moreover, we introduce a line segment ranking loss to stabilize rankings during training, thereby enhancing the generalization capability of the model. Experimental results demonstrate that our method outperforms other Transformer-based and CNN-based approaches in prediction accuracy while requiring fewer training epochs than previous Transformer-based models.

Related papers

• REER-PT: Reverse-Engineered Reasoning for Perplexity-Guided Pre-training Data Augmentation [64.84437332310516]
  We introduce textbfREER-PT, a scalable framework that extends Reverse-Engineered Reasoning (REER) to raw pre-training data.<n>REER-PT identifies continuations that are difficult to predict but can still be inferred from the preceding context, and inserts concise reasoning annotations that reconstruct the missing connection between context and continuation.<n>This sparse transformation preserves the source text and remains compatible with standard next-token prediction, avoiding online reasoning rollouts during pre-training.
  arXiv  Detail & Related papers  (2026-08-31T11:34:19Z)
• AOI-SSL: Self-Supervised Framework for Efficient Segmentation of Wire-bonded Semiconductors In Optical Inspection [8.992928995791724]
  AOI-SSL is a training-efficient framework for semantic segmentation of wire-bonded semiconductors.<n>We pre-train SOTA self-supervised algorithms in a small industrial inspection dataset.<n>We show that, in this setting, simple similarity-based retrieval performs on par with more complex attention-based aggregation.
  arXiv  Detail & Related papers  (2026-05-12T17:27:25Z)
• Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping [66.25536973294726]
  Post-training methods for text-to-image (T2I) models are prone to reward hacking.<n>Super-Linear Advantage Shaping (SLAS) reshapes the local policy space.<n>SLAS consistently surpasses the DanceGRPO baseline across multiple backbones and benchmarks.
  arXiv  Detail & Related papers  (2026-05-11T17:59:25Z)
• Balancing Stability and Plasticity in Sequentially Trained Early-Exiting Neural Networks [11.704570155438887]
  Early-exiting neural networks enable adaptive inference by allowing inputs to exit at intermediate classifiers.<n>We propose two alternative approaches that operate at different levels of the model.<n> Experiments on standard benchmarks show that our approaches consistently improve early-exit performance.
  arXiv  Detail & Related papers  (2026-05-06T18:36:32Z)
• Improving DNS Exfiltration Detection via Transformer Pretraining [0.0]
  In-domain pretraining of Bidirectional Representations from Transformer (BERT) model improves subdomain-level detection of exfiltration at low false positive rates.<n>We develop a controlled pipeline where we freeze operating points on validation and transfer them to the test set.
  arXiv  Detail & Related papers  (2026-04-09T15:58:34Z)
• The Golden Subspace: Where Efficiency Meets Generalization in Continual Test-Time Adaptation [86.83038620308423]
  Continual Test-Time Adaptation (CTTA) aims to enable models to adapt online to unlabeled data streams under distribution shift without accessing source data.<n>We propose Guided Online Low-rank Directional adaptation (GOLD), which uses a lightweight adapter to project features onto the golden subspace and learns a compact scaling vector while the subspace is dynamically updated via AGOP.
  arXiv  Detail & Related papers  (2026-03-23T12:48:38Z)
• MetaDAT: Generalizable Trajectory Prediction via Meta Pre-training and Data-Adaptive Test-Time Updating [18.84791793147564]
  Existing trajectory prediction methods exhibit significant performance degradation under distribution shifts during test time.<n>We propose a meta-learning framework to directly optimize the predictor for fast and accurate online adaptation.<n>At test time, we introduce a data-adaptive model updating mechanism that dynamically adjusts the learning rates and updating frequencies based on online partial derivatives and hard sample selection.<n>Results demonstrate that our method achieves superior adaptation accuracy, surpassing state-of-the-art test-time training methods for trajectory prediction.
  arXiv  Detail & Related papers  (2026-03-10T09:34:32Z)
• Boosted Training of Lightweight Early Exits for Optimizing CNN Image Classification Inference [47.027290803102666]
  We introduce a sequential training approach that aligns branch training with inference-time data distributions.<n>Experiments on the CINIC-10 dataset with a ResNet18 backbone demonstrate that BTS-EE consistently outperforms non-boosted training.<n>These results offer practical efficiency gains for applications such as industrial inspection, embedded vision, and UAV-based monitoring.
  arXiv  Detail & Related papers  (2025-09-10T06:47:49Z)
• Failure Prediction Is a Better Performance Proxy for Early-Exit Networks Than Calibration [4.73982447569174]
  We argue that calibration metrics can be misleading indicators of multi-exit model performance.<n>We propose using failure prediction as a more informative proxy for early-exit model performance.
  arXiv  Detail & Related papers  (2025-08-29T10:21:46Z)
• Segmented Confidence Sequences and Multi-Scale Adaptive Confidence Segments for Anomaly Detection in Nonstationary Time Series [0.0]
  We introduce and empirically evaluate two novel adaptive thresholding frameworks: Segmented Confidence Sequences (SCS) and Multi-Scale Adaptive Confidence Segments (MACS)<n>Our experiments across Wafer Manufacturing benchmark datasets show significant F1-score improvement compared to traditional percentile and rolling quantile approaches.
  arXiv  Detail & Related papers  (2025-08-08T18:34:54Z)
• LINEA: Fast and Accurate Line Detection Using Scalable Transformers [0.0]
  Line detection is a basic digital image processing operation used by higher-level processing methods.<n>Recent transformer-based methods for line detection have proven to be more accurate than methods based on CNNs, at the expense of significantly lower inference speeds.<n>This paper develops a new transformer-based method that is significantly faster without requiring pretraining the attention mechanism on large datasets.
  arXiv  Detail & Related papers  (2025-05-22T05:56:10Z)
• Re-evaluating Group Robustness via Adaptive Class-Specific Scaling [47.41034887474166]
  Group distributionally robust optimization is a prominent algorithm used to mitigate spurious correlations and address dataset bias.<n>Existing approaches have reported improvements in robust accuracies but come at the cost of average accuracy due to inherent trade-offs.<n>We propose a class-specific scaling strategy, directly applicable to existing debiasing algorithms with no additional training.<n>We develop an instance-wise adaptive scaling technique to alleviate this trade-off, even leading to improvements in both robust and average accuracies.
  arXiv  Detail & Related papers  (2024-12-19T16:01:51Z)
• Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach [14.958884168060097]
  We present a novel approach for test-time adaptation via online self-training.<n>Our approach combines concepts in betting martingales and online learning to form a detection tool capable of reacting to distribution shifts.<n> Experimental results demonstrate that our approach improves test-time accuracy under distribution shifts while maintaining accuracy and calibration in their absence.
  arXiv  Detail & Related papers  (2024-08-14T12:40:57Z)
• Domain-adaptive and Subgroup-specific Cascaded Temperature Regression for Out-of-distribution Calibration [16.930766717110053]
  We propose a novel meta-set-based cascaded temperature regression method for post-hoc calibration. We partition each meta-set into subgroups based on predicted category and confidence level, capturing diverse uncertainties. A regression network is then trained to derive category-specific and confidence-level-specific scaling, achieving calibration across meta-sets.
  arXiv  Detail & Related papers  (2024-02-14T14:35:57Z)
• Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization [64.36097398869774]
  Semi-supervised learning (SSL) has been an active research topic for large-scale 3D scene understanding. The existing SSL-based methods suffer from severe training bias due to class imbalance and long-tail distributions of the point cloud data. We introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively.
  arXiv  Detail & Related papers  (2024-01-13T04:16:40Z)
• In-Context Convergence of Transformers [63.04956160537308]
  We study the learning dynamics of a one-layer transformer with softmax attention trained via gradient descent. For data with imbalanced features, we show that the learning dynamics take a stage-wise convergence process.
  arXiv  Detail & Related papers  (2023-10-08T17:55:33Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Towards Better Certified Segmentation via Diffusion Models [62.21617614504225]
  segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. In this paper, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models.
  arXiv  Detail & Related papers  (2023-06-16T16:30:39Z)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification [2.922007656878633]
  We propose a new clustering method based on Variational Bayesian inference, further improved by Adaptive Dimension Reduction. Our proposed method significantly improves accuracy in the realistic unbalanced transductive setting on various Few-Shot benchmarks.
  arXiv  Detail & Related papers  (2022-09-18T10:29:02Z)
• Semantic Self-adaptation: Enhancing Generalization with a Single Sample [45.111358665370524]
  We propose a self-adaptive approach for semantic segmentation. It fine-tunes the parameters of convolutional layers to the input image using consistency regularization. Our empirical study suggests that self-adaptation may complement the established practice of model regularization at training time.
  arXiv  Detail & Related papers  (2022-08-10T12:29:01Z)
• Sample-Efficient Optimisation with Probabilistic Transformer Surrogates [66.98962321504085]
  This paper investigates the feasibility of employing state-of-the-art probabilistic transformers in Bayesian optimisation. We observe two drawbacks stemming from their training procedure and loss definition, hindering their direct deployment as proxies in black-box optimisation. We introduce two components: 1) a BO-tailored training prior supporting non-uniformly distributed points, and 2) a novel approximate posterior regulariser trading-off accuracy and input sensitivity to filter favourable stationary points for improved predictive performance.
  arXiv  Detail & Related papers  (2022-05-27T11:13:17Z)
• Consistent Accelerated Inference via Confident Adaptive Transformers [29.034390810078172]
  We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers. We simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. We demonstrate the effectiveness of this approach on four classification and regression tasks.
  arXiv  Detail & Related papers  (2021-04-18T10:22:28Z)

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.