Method for one-shot visual search of object parts / one-shot semantic part correspondence. Given a single reference image of an object with annotated affordance regions, it segments semantically corresponding parts within a target scene. AffCorrs is used to find corresponding affordances both for intra- and inter-class one-shot part segmentation.

**Big-Little Net** is a convolutional neural network architecture for learning multi-scale feature representations. This is achieved by using a multi-branch network, which has different computational complexity at different branches with different resolutions. Through frequent merging of features from branches at distinct scales, the model obtains multi-scale features while using less computation.

It consists of Big-Little Modules, which have two branches: each of which represents a separate block from a deep model and a less deep counterpart. The two branches are fused with linear combination + unit weights. These two branches are known as Big-Branch (more layers and channels at low resolutions) and Little-Branch (fewer layers and channels at high resolution).

Big-Little Net

Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition

AffCorrs

A **Multiscale Dilated Convolution Block** is an Inception-style convolutional block motivated by the ideas that image features naturally occur at multiple scales, that a network’s expressivity is proportional to the range of functions it can represent divided by its total number of parameters, and by the desire to efficiently expand a network’s receptive field. The Multiscale [Dilated Convolution](https://paperswithcode.com/method/dilated-convolution) (MDC) block applies a single $F\times{F}$ filter at multiple dilation factors, then performs a weighted elementwise sum of each dilated filter’s output, allowing the network to simultaneously learn a set of features and the relevant scales at which those features occur with a minimal increase in parameters. This also rapidly expands the network’s receptive field without requiring an increase in depth or the number of parameters.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com