**BasicVSR** is a video super-resolution pipeline including optical flow and [residual blocks](https://paperswithcode.com/method/residual-connection). It adopts a typical bidirectional recurrent network. The upsampling module $U$ contains multiple [pixel-shuffle](https://paperswithcode.com/method/pixelshuffle) and convolutions. In the Figure, red and blue colors represent the backward and forward propagations, respectively.  The propagation branches contain only generic components. $S, W$, and $R$ refer to the flow estimation module, spatial warping module, and residual blocks, respectively.

**Cascade R-CNN** is an object detection architecture that seeks to address problems with degrading performance with increased IoU thresholds (due to overfitting during training and inference-time mismatch between IoUs for which detector is optimal and the inputs). It is a multi-stage extension of the [R-CNN](https://paperswithcode.com/method/r-cnn), where detector stages deeper into the cascade are sequentially more selective against close false positives. The cascade of R-CNN stages are trained sequentially, using the output of one stage to train the next. This is motivated by the observation that the output IoU of a regressor is almost invariably better than the input IoU. 

Cascade R-CNN does not aim to mine hard negatives. Instead, by adjusting bounding boxes, each stage aims to find a good set of close false positives for training the next stage. When operating in this manner, a sequence of detectors adapted to increasingly higher IoUs can beat the overfitting problem, and thus be effectively trained. At inference, the same cascade procedure is applied. The progressively improved hypotheses are better matched to the increasing detector quality at each stage.

Cascade R-CNN

Cascade R-CNN: Delving into High Quality Object Detection

BasicVSR

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

An **Inception Module** is an image model block that aims to approximate an optimal local sparse structure in a CNN. Put simply, it allows for us to use multiple types of filter size, instead of being restricted to a single filter size, in a single image block, which we then concatenate and pass onto the next layer.

Source	BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com