An unsupervised approach for identifying Hierarchical Information Threads by analysing the network of related articles in a collection. In particular, HINT leverages article timestamps and the 5W1H questions to identify related articles about an event or discussion. HINT then constructs a network representation of the articles,
and identify threads as strongly connected hierarchical network communities.

**TrIVD-GAN**, or **Transformation-based & TrIple Video Discriminator GAN**, is a type of generative adversarial network for video generation that builds upon [DVD-GAN](https://paperswithcode.com/method/dvd-gan). Improvements include a novel transformation-based recurrent unit (the TSRU) that makes the generator more expressive, and an improved discriminator architecture. 

In contrast with DVD-[GAN](https://paperswithcode.com/method/gan), TrIVD-GAN has an alternative split for the roles of the discriminators, with $\mathcal{D}\_{S}$ judging per-frame global structure, while $\mathcal{D}\_{T}$ critiques local spatiotemporal structure. This is achieved by downsampling the $k$ randomly sampled frames fed to $\mathcal{D}\_{S}$ by a factor $s$, and cropping $T \times H/s \times W/s$ clips inside the high resolution video fed to $\mathcal{D}\_{T}$, where $T, H, W, C$ correspond to time, height, width and channel dimension of the input. This further reduces the number of pixels to process per video,
from $k \times H \times W + T \times H/s \times W/s$ to $\left(k + T\right) \times H/s \times W/s$.

TrIVD-GAN

Transformation-based Adversarial Video Prediction on Large-Scale Data

HINT

Effective Hierarchical Information Threading Using Network Community Detection

**PSANet** is a semantic segmentation architecture that utilizes a [Point-wise Spatial Attention](https://paperswithcode.com/method/point-wise-spatial-attention) (PSA) module to aggregate long-range contextual information in a flexible and adaptive manner. Each position in the feature map is connected with all other ones through self-adaptively predicted attention maps, thus harvesting various information nearby and far away. Furthermore, the authors design the bi-directional information propagation path for a comprehensive understanding of complex scenes. Each position collects information from all others to help the prediction of itself and vice versa, the information at each position can be distributed globally, assisting the prediction of all other positions. Finally, the bi-directionally aggregated contextual information is fused with local features to form the final representation of complex scenes.

The authors use [ResNet](https://paperswithcode.com/method/resnet) as an [FCN](https://paperswithcode.com/method/fcn) backbone for PSANet, as the Figure to the right illustrates. The proposed PSA module is then used to aggregate long-range contextual information from the local representation. It follows stage-5 in ResNet, which is the final stage of the FCN backbone. Features in stage-5 are semantically stronger. Aggregating them together leads to a more comprehensive representation of long-range context. Moreover, the spatial size of the feature map at stage-5 is smaller and can reduce computation overhead and memory consumption. An auxiliary loss branch is applied apart from the main loss.

Source	Effective Hierarchical Information Threading Using Network Community Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com