**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.

**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 $X$.

Cross-Scale Non-Local Attention

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

AVSlowFast

Audiovisual SlowFast Networks for Video Recognition

**Hybrid Firefly and Particle Swarm Optimization (HFPSO)** is a metaheuristic optimization algorithm that combines strong points of firefly and particle swarm optimization. HFPSO tries to determine the start of the local search process properly by checking the previous global best fitness values.

[Click Here for the Paper](https://www.sciencedirect.com/science/article/abs/pii/S156849461830084X)

[Codes (MATLAB)](https://www.mathworks.com/matlabcentral/fileexchange/67768-a-hybrid-firefly-and-particle-swarm-optimization-hfpso)

Source	Audiovisual SlowFast Networks for Video Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com