**Continuous Bag-of-Words Word2Vec** is an architecture for creating word embeddings that uses $n$ future words as well as $n$ past words to create a word embedding. The objective function for CBOW is:

$$ J\_\theta = \frac{1}{T}\sum^{T}\_{t=1}\log{p}\left(w\_{t}\mid{w}\_{t-n},\ldots,w\_{t-1}, w\_{t+1},\ldots,w\_{t+n}\right) $$

In the CBOW model, the distributed representations of context are used to predict the word in the middle of the window. This contrasts with [Skip-gram Word2Vec](https://paperswithcode.com/method/skip-gram-word2vec) where the distributed representation of the input word is used to predict the context.

**FoveaBox** is anchor-free framework for object detection. Instead of using predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations for each input image

It is a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone is responsible for computing a convolutional feature map over an entire input image and is an off-the-shelf convolutional network. The first subnet performs per pixel classification on the backbone’s output; the second subnet performs bounding box prediction for the corresponding
position.

FoveaBox

FoveaBox: Beyond Anchor-based Object Detector

CBoW Word2Vec

Efficient Estimation of Word Representations in Vector Space

**ViP-DeepLab** is a model for depth-aware video panoptic segmentation. It extends Panoptic-[DeepLab](https://paperswithcode.com/method/deeplab) by adding a depth prediction head to perform monocular depth estimation and a next-frame instance branch which regresses to the object centers in frame $t$ for frame $t + 1$.  This allows the model to jointly perform video panoptic segmentation and monocular depth estimation.

Source	Efficient Estimation of Word Representations in Vector Space
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com