preprintJul 1, 2017Closed access

Fully Convolutional Instance-Aware Semantic Segmentation

Microsoft Research Asia (China) · Tsinghua University

Indexed incrossref

Abstract

We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation [29] and instance mask proposal [5]. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest. The network architecture is highly integrated and efficient. It achieves state-of-the-art performance in both accuracy and efficiency. It wins the COCO 2016 segmentation competition by a large margin. Code would be released at https://github.com/daijifeng001/TA-FCN.

Citation impact

1,131
total citations
FWCI
51.38
Percentile
100%
References
66
Citations per year

Authors

5

Topics & keywords

Keywords
  • Computer science
  • Segmentation
  • Margin (machine learning)
  • Artificial intelligence
  • Representation (politics)
  • Convolutional neural network
  • Task (project management)
  • Code (set theory)
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