Interpolation between [exponential decay](https://paperswithcode.com/method/exponential-decay) and [cosine annealing](https://paperswithcode.com/method/cosine-annealing).

**ClipBERT** is a framework for end-to-end-learning for video-and-language tasks, by employing sparse sampling, where only a single or a few sparsely sampled short clips from a video are used at each training step. Two aspects distinguish ClipBERT from previous work. 

First, in contrast to densely extracting video features (adopted by most existing methods), CLIPBERT sparsely samples only one single or a few short clips from the full-length videos at each training step. The hypothesis is that visual features from sparse clips already capture key visual and semantic information in the video, as consecutive clips usually contain similar semantics from a continuous scene. Thus, a handful of clips are sufficient for training, instead of using the full video. Then, predictions from multiple densely-sampled clips are aggregated to obtain the final video-level prediction during inference, which is less computational demanding. 

The second differentiating aspect concerns the initialization of model weights (i.e., transfer through pre-training). The authors use 2D architectures (e.g., [ResNet](https://paperswithcode.com/method/resnet)-50) instead of 3D features as the visual backbone for video encoding, allowing them to harness the power of image-text pretraining for video-text understanding along with the advantages of low memory cost and runtime efficiency.

ClipBERT

Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling

Cosine Power Annealing

sharpDARTS: Faster and More Accurate Differentiable Architecture Search

The full architecture of CGNN is presented at [CGNN's official site](https://tony-y.github.io/cgnn/architectures/).

Source	sharpDARTS: Faster and More Accurate Differentiable Architecture Search
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com