mlsanityData to 7 Oct 2026

Inharmonious Region Localization

arXiv 2104.09453 ↗First posted 19 Apr 2021cs.CVcited by 5

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Abstract (arXiv)

The advance of image editing techniques allows users to create artistic works, but the manipulated regions may be incompatible with the background. Localizing the inharmonious region is an appealing yet challenging task. Realizing that this task requires effective aggregation of multi-scale contextual information and suppression of redundant information, we design novel Bi-directional Feature Integration (BFI) block and Global-context Guided Decoder (GGD) block to fuse multi-scale features in the encoder and decoder respectively. We also employ Mask-guided Dual Attention (MDA) block between the encoder and decoder to suppress the redundant information. Experiments on the image harmonization dataset demonstrate that our method achieves competitive performance for inharmonious region localization. The source code is available at https://github.com/bcmi/DIRL.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

21 of 32 references.

References · 21

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9 more
  1. 2017Deep Image Harmonizationcited by 120
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  5. 2019Improving the Harmony of the Composite Image by Spatial-Separated Attention Modulecited by 73
  6. 2017Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)cited by 64
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4.

5 papers.

  1. 2021Making Images Real Again: A Comprehensive Survey on Deep Image Compositioncited by 63
  2. 2022Semantic-guided Multi-Mask Image Harmonizationcited by 6
  3. 2022Inharmonious Region Localization by Magnifying Domain Discrepancycited by 2
  4. 2022Inharmonious Region Localization with Auxiliary Style Featurecited by 1
  5. 2022Inharmonious Region Localization via Recurrent Self-Reasoningcited by 0