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CARAFE: Content-Aware ReAssembly of FEatures

Abstract

Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose Content-Aware ReAssembly of FEatures (CARAFE), a universal, lightweight and highly effective operator to fulfill this goal. CARAFE has several appealing properties: (1) Large field of view. Unlike previous works (e.g. bilinear interpolation) that only exploit sub-pixel neighborhood, CARAFE can aggregate contextual information within a large receptive field. (2) Content-aware handling. Instead of using a fixed kernel for all samples (e.g. deconvolution), CARAFE enables instance-specific content-aware handling, which generates adaptive kernels on-the-fly. (3) Lightweight and fast to compute. CARAFE introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting. CARAFE shows consistent and substantial gains across all the tasks (1.2%, 1.3%, 1.8%, 1.1db respectively) with negligible computational overhead. It has great potential to serve as a strong building block for future research. It has great potential to serve as a strong building block for future research.

Citation

We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures.

@inproceedings{Wang_2019_ICCV,
    title = {CARAFE: Content-Aware ReAssembly of FEatures},
    author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {October},
    year = {2019}
}

Results and Models

The results on COCO 2017 val is shown in the below table.

Method Backbone Style Lr schd Test Proposal Num Inf time (fps) Box AP Mask AP Config Download
Faster R-CNN w/ CARAFE R-50-FPN pytorch 1x 1000 16.5 38.6 38.6 config model | log
- - - - 2000
Mask R-CNN w/ CARAFE R-50-FPN pytorch 1x 1000 14.0 39.3 35.8 config model | log
- - - - 2000

Implementation

The CUDA implementation of CARAFE can be find at https://github.com/myownskyW7/CARAFE.