**Generative Adversarial Imitation Learning** presents a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning.

Inspired by the spatial propagation mechanism utilized in the depth completion task \cite{NLSPN}, we introduce a normal incorporated non-local disparity propagation module in which we hub NDP to generate non-local affinities and offsets for spatial propagation at the disparity level. The motivation lies that the sampled pixels for edges and occluded regions are supposed to be selected. The propagation process aggregates disparities via plane affinity relations, which alleviates the phenomenon of disparity blurring at object edges due to frontal parallel windows. And the disparities in occluded areas are also optimized at the same time by being propagated from non-occluded areas where the predicted disparities are with high confidence.

Spatial Propagation

Digging Into Normal Incorporated Stereo Matching

GAIL

Generative Adversarial Imitation Learning

**Visformer**, or **Vision-friendly Transformer**, is an architecture that combines [Transformer](https://paperswithcode.com/methods/category/transformers)-based architectural features with those from [convolutional neural network](https://paperswithcode.com/methods/category/convolutional-neural-networks) architectures. Visformer adopts the stage-wise design for higher base performance. But [self-attentions](https://paperswithcode.com/method/multi-head-attention) are only utilized in the last two stages, considering that self-attention in the high-resolution stage is relatively inefficient even when the FLOPs are balanced. Visformer employs [bottleneck blocks](https://paperswithcode.com/method/bottleneck-residual-block) in the first stage and utilizes [group 3 × 3 convolutions](https://paperswithcode.com/method/grouped-convolution) in bottleneck blocks inspired by [ResNeXt](https://paperswithcode.com/method/resnext). It also introduces [BatchNorm](https://paperswithcode.com/method/batch-normalization) to patch embedding modules as in CNNs.

Source	Generative Adversarial Imitation Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com