**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).

**Skip-gram Word2Vec** is an architecture for computing word embeddings. Instead of using surrounding words to predict the center word, as with CBow Word2Vec, Skip-gram Word2Vec uses the central word to predict the surrounding words.

The skip-gram objective function sums the log probabilities of the surrounding $n$ words to the left and right of the target word $w\_{t}$ to produce the following objective:

$$J\_\theta = \frac{1}{T}\sum^{T}\_{t=1}\sum\_{-n\leq{j}\leq{n}, \neq{0}}\log{p}\left(w\_{j+1}\mid{w\_{t}}\right)$$

Skip-gram Word2Vec

Efficient Estimation of Word Representations in Vector Space

Big-Little Net

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

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.

Source	Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com