Combinatorics

Pure mathematics

Bilinear interpolation

Key (lock)

Feature (linguistics)

Block (permutation group theory)

Mathematics

Kernel (algebra)

Image (mathematics)

Upsampling

Computer engineering

Inpainting

Machine learning

Philosophy

Artificial intelligence

Exploit

Programming language

Geometry

Object detection

Overhead (engineering)

Computer science

Linguistics

Field (mathematics)

Computer vision

Computer security

Segmentation

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 subpixel 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% AP, 1.3% AP, 1.8% mIoU, 1.1dB respectively) with negligible computational overhead. It has great potential to serve as a strong building block for future research. Code and models are available at https://github.com/open-mmlab/mmdetection.

CARAFE: Content-Aware ReAssembly of FEatures