**Cross-Scale Non-Local Attention**, or **CS-NL**,  is a non-local attention module for image super-resolution deep networks. It learns to mine long-range dependencies between LR features to larger-scale HR patches within the same feature map. Specifically, suppose we are conducting an s-scale super-resolution with the module, given a feature map $X$ of spatial size $(W, H)$, we first bilinearly downsample it to $Y$ with scale $s$, and match the $p\times p$ patches in $X$ with the downsampled $p \times p$ candidates in $Y$ to obtain the [softmax](https://paperswithcode.com/method/softmax) matching score. Finally, we conduct deconvolution.on the score by weighted adding the patches of size $\left(sp, sp\right)$ extracted from $X$. The obtained $Z$ of size $(sW, sH)$ will be $s$ times super-resolved than $X$.

**$\epsilon$-Greedy Exploration** is an exploration strategy in reinforcement learning that takes an exploratory action with probability $\epsilon$ and a greedy action with probability $1-\epsilon$. It tackles the exploration-exploitation tradeoff with reinforcement learning algorithms: the desire to explore the state space with the desire to seek an optimal policy. Despite its simplicity, it is still commonly used as an behaviour policy $\pi$ in several state-of-the-art reinforcement learning models.

Image Credit: [Robin van Embden](https://cran.r-project.org/web/packages/contextual/vignettes/sutton_barto.html)

Epsilon Greedy Exploration

Cross-Scale Non-Local Attention

Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

**Audiovisual SlowFast Network**, or **AVSlowFast**, is an architecture for integrated audiovisual perception. AVSlowFast has Slow and Fast visual pathways that are integrated with a Faster Audio pathway to model vision and sound in a unified representation. Audio and visual features are fused at multiple layers, enabling audio to contribute to the formation of hierarchical audiovisual concepts. To overcome training difficulties that arise from different learning dynamics for audio and visual modalities, [DropPathway](https://paperswithcode.com/method/droppathway) is used, which randomly drops the Audio pathway during training as an effective regularization technique. Inspired by prior studies in neuroscience, hierarchical audiovisual synchronization is performed to learn joint audiovisual features.

Source	Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com