Edge-aware Graph Representation Learning and Reasoning for Face Parsing

• URL: http://arxiv.org/abs/2007.11240v1
• Date: Wed, 22 Jul 2020 07:46:34 GMT
• Title: Edge-aware Graph Representation Learning and Reasoning for Face Parsing
• Authors: Gusi Te, Yinglu Liu, Wei Hu, Hailin Shi, and Tao Mei
• Abstract summary: Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently. Previous methods have shown their efficiency in face parsing, which however overlook the correlation among different face regions. We propose to model and reason the region-wise relations by learning graph representations.
• Score: 61.5045850197694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently. Previous methods have shown their efficiency in face parsing, which however overlook the correlation among different face regions. The correlation is a critical clue about the facial appearance, pose, expression etc., and should be taken into account for face parsing. To this end, we propose to model and reason the region-wise relations by learning graph representations, and leverage the edge information between regions for optimized abstraction. Specifically, we encode a facial image onto a global graph representation where a collection of pixels ("regions") with similar features are projected to each vertex. Our model learns and reasons over relations between the regions by propagating information across vertices on the graph. Furthermore, we incorporate the edge information to aggregate the pixel-wise features onto vertices, which emphasizes on the features around edges for fine segmentation along edges. The finally learned graph representation is projected back to pixel grids for parsing. Experiments demonstrate that our model outperforms state-of-the-art methods on the widely used Helen dataset, and also exhibits the superior performance on the large-scale CelebAMask-HQ and LaPa dataset. The code is available at https://github.com/tegusi/EAGRNet.

Related papers

• RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation [18.053914853235142]
  We propose a novel representation of an environment based on "image segments" Unlike 3D scene graphs, we create a purely topological graph with segments as nodes. This unveils a "continuous sense of a place", defined by inter-image persistence of segments.
  arXiv  Detail & Related papers  (2024-05-09T14:17:26Z)
• BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation [21.912509900254364]
  We apply graph convolution into the segmentation task and propose an improved textitLaplacian. Our method outperforms the state-of-the-art approaches on the segmentation of polyps in colonoscopy images and of the optic disc and optic cup in colour fundus images.
  arXiv  Detail & Related papers  (2021-10-27T21:12:27Z)
• Edge but not Least: Cross-View Graph Pooling [76.71497833616024]
  This paper presents a cross-view graph pooling (Co-Pooling) method to better exploit crucial graph structure information. Through cross-view interaction, edge-view pooling and node-view pooling seamlessly reinforce each other to learn more informative graph-level representations.
  arXiv  Detail & Related papers  (2021-09-24T08:01:23Z)
• Learning to Generate Scene Graph from Natural Language Supervision [52.18175340725455]
  We propose one of the first methods that learn from image-sentence pairs to extract a graphical representation of localized objects and their relationships within an image, known as scene graph. We leverage an off-the-shelf object detector to identify and localize object instances, match labels of detected regions to concepts parsed from captions, and thus create "pseudo" labels for learning scene graph.
  arXiv  Detail & Related papers  (2021-09-06T03:38:52Z)
• Font Completion and Manipulation by Cycling Between Multi-Modality Representations [113.26243126754704]
  We innovate to explore the generation of font glyphs as 2D graphic objects with the graph as an intermediate representation. We formulate a cross-modality cycled image-to-image structure with a graph between an image encoder and an image. Our model generates improved results than both image-to-image baseline and previous state-of-the-art methods for glyph completion.
  arXiv  Detail & Related papers  (2021-08-30T02:43:29Z)
• Edge Representation Learning with Hypergraphs [36.03482700241067]
  We propose a novel edge representation learning framework based on Dual Hypergraph Transformation (DHT), which transforms the edges of a graph into the nodes of a hypergraph. We validate our edge representation learning method with hypergraphs on diverse graph datasets for graph representation and generation performance. Our edge representation learning and pooling method also largely outperforms state-of-the-art graph pooling methods on graph classification.
  arXiv  Detail & Related papers  (2021-06-30T06:59:05Z)
• Segmentation-grounded Scene Graph Generation [47.34166260639392]
  We propose a framework for pixel-level segmentation-grounded scene graph generation. Our framework is agnostic to the underlying scene graph generation method. It is learned in a multi-task manner with both target and auxiliary datasets.
  arXiv  Detail & Related papers  (2021-04-29T08:54:08Z)
• Adaptive Graph Representation Learning and Reasoning for Face Parsing [55.086151726427104]
  Face parsing infers a pixel-wise label to each facial component. Component-wise relationship is a critical clue in discriminating ambiguous pixels in facial area. We propose adaptive graph representation learning and reasoning over facial components.
  arXiv  Detail & Related papers  (2021-01-18T12:17:40Z)

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.