**ParamCrop** is a parametric cubic cropping for video contrastive learning, where cubic cropping refers to cropping a 3D cube
from the input video. The central component of ParamCrop is a differentiable spatio-temporal cropping operation, which enables ParamCrop to be trained simultaneously with the video backbone and adjust the cropping strategy on the fly. The objective of ParamCrop is set to be adversarial to the video backbone, which is to increase the contrastive loss. Hence, initialized with the simplest setting where two cropped views largely overlaps, ParamCrop gradually increases the disparity between two views.

**VEGA** is an AutoML framework that is compatible and optimized for multiple hardware platforms. It integrates various modules of AutoML, including [Neural Architecture Search](https://paperswithcode.com/method/neural-architecture-search) (NAS), Hyperparameter Optimization (HPO), Auto Data Augmentation, Model Compression, and Fully Train. To support a variety of search algorithms and tasks, it involves a fine-grained search space and a description language to enable easy adaptation to different search algorithms and tasks.

VEGA

VEGA: Towards an End-to-End Configurable AutoML Pipeline

ParamCrop

ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning

**RIFE**, or **Real-time Intermediate Flow Estimation** is an intermediate flow estimation algorithm for Video Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate the bi-directional optical flows, then scale and reverse them to approximate intermediate flows, leading to artifacts on motion boundaries. RIFE uses a neural network named [IFNet](https://paperswithcode.com/method/ifnet) that can directly estimate the intermediate flows from coarse-to-fine with much better speed. It introduces a privileged distillation scheme for training intermediate flow model, which leads to a large performance improvement.

In RIFE training, given two input frames $I_{0}, I_{1}$, we directly feed them into the IFNet to approximate intermediate flows $F_{t \rightarrow 0}, F_{t \rightarrow 1}$ and the fusion map $M$. During training phase, a privileged teacher refines student's results to get $F_{t \rightarrow 0}^{T e a}, F_{t \rightarrow 1}^{T e a}$ and $M^{\text {Tea }}$ based on ground truth $I_{t}$. The student model and the teacher model are jointly trained from scratch using the reconstruction loss. The teacher's approximations are more accurate so that they can guide the student to learn.

Source	ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com