**DIoU-NMS** is a type of non-maximum suppression where we use Distance IoU rather than regular DIoU, in which the overlap area and the distance between two central points of bounding boxes are simultaneously considered when suppressing redundant boxes.

In original NMS, the IoU metric is used to suppress the redundant detection boxes, where the overlap area is the unique factor, often yielding false suppression for the cases with occlusion. With DIoU-NMS, we not only consider the overlap area but also central point distance between two boxes.

**SPADE**, or **Spatially-Adaptive Normalization** is a conditional normalization method for semantic image synthesis. Similar to [Batch Normalization](https://www.paperswithcode.com/method/batch-normalization), the activation is normalized in the channel-wise manner and then modulated with learned scale and bias. In the SPADE, the mask is first projected onto an embedding space and then convolved to produce the modulation parameters $\gamma$ and $\beta .$ Unlike prior conditional normalization methods, $\gamma$ and $\mathbf{\beta}$ are not vectors, but tensors with spatial dimensions. The produced $\gamma$ and $\mathbf{\beta}$ are multiplied and added to the normalized activation element-wise.

SPADE

Semantic Image Synthesis with Spatially-Adaptive Normalization

DIoU-NMS

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

The **Compressive Transformer** is an extension to the [Transformer](https://paperswithcode.com/method/transformer) which maps past hidden activations (memories) to a smaller set of compressed representations (compressed memories). The Compressive Transformer uses the same attention mechanism over its set of memories and compressed memories, learning to query both its short-term granular memory and longer-term coarse memory. It builds on the ideas of [Transformer-XL](https://paperswithcode.com/method/transformer-xl) which maintains a memory of past activations at each layer to preserve a longer history of context. The Transformer-XL discards past activations when they become sufficiently old (controlled by the size of the memory). The key principle of the Compressive Transformer is to compress these old memories, instead of discarding them, and store them in an additional [compressed memory](https://paperswithcode.com/method/compressed-memory).

At each time step $t$, we discard the oldest compressed memories (FIFO) and then the oldest $n$ states from ordinary memory are compressed and shifted to the new slot in compressed memory. During training, the compressive memory component is optimized separately from the main language model (separate training loop).

Source	Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com