Adaptively Spatial Feature Fusion

What is ASFF?

ASFF, which stands for Adaptively Spatial Feature Fusion, is a powerful method for pyramidal feature fusion. Essentially, it helps neural networks learn how to spatially filter and combine features from multiple levels in a pyramid, in order to create more accurate object detection models. ASFF helps to suppress inconsistent or conflicting information by selecting only the most useful features for combination.

How does ASFF work?

ASFF operates by first integrating and resizing the features from different levels into the same resolution. Then, at each spatial location, the features at different levels are fused adaptively based on their importance. Some features may be filtered out if they carry contradictory information, while others may be dominant with more discriminative clues.

The output feature maps from ASFF are created by combining each level's features according to their spatial importance weights. These weights are learned by the network and can be represented by scalar variables, which are shared across all channels. The softmax function is used to compute these importance weights, and a control parameter is used to ensure that the sum of all weights equals 1.

Why is ASFF important?

ASFF is a major breakthrough in the field of object detection because it improves the scale-invariance of features. This means that object detection models built using ASFF are more accurate when it comes to detecting objects of varying sizes. Additionally, ASFF is agnostic to the backbone model and can be applied to single-shot detectors with a feature pyramid structure, making it a flexible and widely applicable technique.

How is ASFF implemented?

ASFF is relatively simple to implement, with only a marginal increase in computational cost. Convolution layers are used to compute the weight scalar maps for each level's features, and these maps are learned through standard back-propagation. The outputs from ASFF can be used for object detection using the same pipeline as YOLOv3, one of the most popular object detection models.

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