OLAF: A Plug-and-Play Framework for Enhanced Multi-object Multi-part Scene Parsing
| Autor: | Gupta, Pranav, Singh, Rishubh, Shenoy, Pradeep, Sarvadevabhatla, Ravikiran |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | Multi-object multi-part scene segmentation is a challenging task whose complexity scales exponentially with part granularity and number of scene objects. To address the task, we propose a plug-and-play approach termed OLAF. First, we augment the input (RGB) with channels containing object-based structural cues (fg/bg mask, boundary edge mask). We propose a weight adaptation technique which enables regular (RGB) pre-trained models to process the augmented (5-channel) input in a stable manner during optimization. In addition, we introduce an encoder module termed LDF to provide low-level dense feature guidance. This assists segmentation, particularly for smaller parts. OLAF enables significant mIoU gains of $\mathbf{3.3}$ (Pascal-Parts-58), $\mathbf{3.5}$ (Pascal-Parts-108) over the SOTA model. On the most challenging variant (Pascal-Parts-201), the gain is $\mathbf{4.0}$. Experimentally, we show that OLAF's broad applicability enables gains across multiple architectures (CNN, U-Net, Transformer) and datasets. The code is available at olafseg.github.io Comment: Accepted in The European Conference on Computer Vision (ECCV) 2024 |
| Databáze: | arXiv |
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