**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.

**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.

CBoW Word2Vec

Efficient Estimation of Word Representations in Vector Space

ViP-DeepLab

ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation

**Anti-Alias Downsampling (AA)** aims to improve the shift-equivariance of deep networks. Max-pooling is inherently composed of two operations. The first operation is to densely evaluate the max operator and second operation is naive subsampling. AA is proposed as a low-pass filter between them to achieve practical anti-aliasing in any existing strided layer such as strided [convolution](https://paperswithcode.com/method/convolution). The smoothing factor can be adjusted by changing the blur kernel filter size, where a larger filter size results in increased blur.

Source	ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com