Harmonious Semantic Line Detection via Maximal Weight Clique Selection

• URL: http://arxiv.org/abs/2104.06903v1
• Date: Wed, 14 Apr 2021 14:54:27 GMT
• Title: Harmonious Semantic Line Detection via Maximal Weight Clique Selection
• Authors: Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong and Chang-Su Kim
• Abstract summary: A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net) Experimental results demonstrate that the proposed algorithm can detect semantic lines effectively and efficiently.
• Score: 42.20986101351654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal process. Third, we construct a complete graph, whose edge weights are computed by H-Net. Finally, we determine a maximal weight clique representing an optimal set of semantic lines. Moreover, to assess the overall harmony of detected lines, we propose a novel metric, called HIoU. Experimental results demonstrate that the proposed algorithm can detect harmonious semantic lines effectively and efficiently. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-MWCS.

Related papers

• Semantic Line Combination Detector [17.60109693530759]
  A novel algorithm, called semantic line combination detector (SLCD), to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets.
  arXiv  Detail & Related papers  (2024-04-29T03:21:05Z)
• PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression [14.38941096136575]
  Symbolic regression aims to derive interpretable symbolic expressions from data in order to better understand and interpret data. In this study, a symbolic network called PruneSymNet is proposed for symbolic regression. A greedy pruning algorithm is proposed to prune the network into a subnetwork while ensuring the accuracy of data fitting.
  arXiv  Detail & Related papers  (2024-01-25T11:53:35Z)
• Online Network Source Optimization with Graph-Kernel MAB [62.6067511147939]
  We propose Grab-UCB, a graph- kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks. We describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy.
  arXiv  Detail & Related papers  (2023-07-07T15:03:42Z)
• Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection [57.34881616131377]
  We introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. We suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs.
  arXiv  Detail & Related papers  (2022-12-23T17:30:19Z)
• Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching [44.10942440861644]
  A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net), comparative ranking and matching networks (R-Net and M-Net)
  arXiv  Detail & Related papers  (2022-03-29T07:00:29Z)
• Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via GDPA Linearization [59.87663954467815]
  Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer. In this paper, leveraging a recent linear algebraic theorem called Gershgorin disc perfect alignment (GDPA), we unroll a projection-free algorithm for semi-definite programming relaxation (SDR) of a binary graph. Experimental results show that our unrolled network outperformed pure model-based graph classifiers, and achieved comparable performance to pure data-driven networks but using far fewer parameters.
  arXiv  Detail & Related papers  (2021-09-10T07:01:15Z)
• Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs [6.084888301899142]
  We consider a learning-based LNS approach for mixed integer programs (MIPs) We train a Neural Diving model to represent a probability distribution over assignments, which, together with an off-the-shelf MIP solver, generates an initial assignment. We train a Neural Neighborhood Selection policy to select a search neighborhood at each step, which is searched using a MIP solver to find the next assignment.
  arXiv  Detail & Related papers  (2021-07-21T16:43:46Z)
• Gated Path Selection Network for Semantic Segmentation [72.44994579325822]
  We develop a novel network named Gated Path Selection Network (GPSNet), which aims to learn adaptive receptive fields. In GPSNet, we first design a two-dimensional multi-scale network - SuperNet, which densely incorporates features from growing receptive fields. To dynamically select desirable semantic context, a gate prediction module is further introduced.
  arXiv  Detail & Related papers  (2020-01-19T12:32:17Z)

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.