**MEI** introduces the *multi-partition embedding interaction* technique with block term tensor format to systematically address the efficiency--expressiveness trade-off in knowledge graph embedding. It divides the embedding vector into multiple partitions and learns the local interaction patterns from data instead of using fixed special patterns as in ComplEx or SimplE models. This enables MEI to achieve optimal efficiency--expressiveness trade-off, not just being fully expressive. Previous methods such as TuckER, RESCAL, DistMult, ComplEx, and SimplE are suboptimal restricted special cases of MEI.

**CascadePSP** is a general segmentation refinement model that refines any given segmentation from low to high resolution. The model takes as input an initial mask that can be an output of any algorithm to provide a rough object location. Then the CascadePSP will output a refined mask. The model is designed in a cascade fashion that generates refined segmentation in a coarse-to-fine manner. Coarse outputs from the early levels predict object structure which will be used as input to the latter levels to refine boundary details.

CascadePSP

CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion

It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions.

Source	Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com