**DetNAS** is a [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) algorithm for the design of better backbones for object detection. It is based on the technique of one-shot supernet, which contains all possible networks in the search space. The supernet is trained under the typical detector training schedule: ImageNet pre-training and detection fine-tuning. Then, the architecture search is performed on the trained supernet, using the detection task as the guidance. DetNAS uses evolutionary search as opposed to RL-based methods or gradient-based methods.

COP-KMeans is a modified version the popular k-means algorithm that supports pairwise constraints. 

Original paper : Constrained K-means Clustering with Background Knowledge, Wagstaff et al. 2001

COP-KMeans

DetNAS

DetNAS: Backbone Search for Object Detection

**Mix-FFN** is a feedforward layer used in the [SegFormer](https://paperswithcode.com/method/segformer) architecture. [ViT](https://www.paperswithcode.com/method/vision-transformer) uses [positional encoding](https://paperswithcode.com/methods/category/position-embeddings) (PE) to introduce the location information. However, the resolution of $\mathrm{PE}$ is fixed. Therefore, when the test resolution is different from the training one, the positional code needs to be interpolated and this often leads to dropped accuracy. To alleviate this problem, [CPVT](https://www.paperswithcode.com/method/cpvt) uses $3 \times 3$ Conv together with the PE to implement a data-driven PE. The authors of Mix-FFN argue that positional encoding is actually not necessary for semantic segmentation. Instead, they use Mix-FFN which considers the effect of zero padding to leak location information, by directly using a $3 \times 3$ Conv in the feed-forward network (FFN). Mix-FFN can be formulated as:

$$
\mathbf{x}\_{\text {out }}=\operatorname{MLP}\left(\operatorname{GELU}\left(\operatorname{Conv}\_{3 \times 3}\left(\operatorname{MLP}\left(\mathbf{x}\_{i n}\right)\right)\right)\right)+\mathbf{x}\_{i n}
$$

where $\mathbf{x}\_{i n}$ is the feature from a self-attention module. Mix-FFN mixes a $3 \times 3$ convolution and an MLP into each FFN.

Source	DetNAS: Backbone Search for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com