公开(公告)号：WO2022260591A1

公开(公告)日：2022-12-15

申请号：PCT/SG2022/050296

申请日：2022-05-10

Applicant: LEMON INC.

Inventor： LIAN, Xiaochen ,  YANG, Linjie ,  WANG, Peng ,  JIN, Xiaojie ,  DING, Mingyu

IPC: G06N3/08 ,  G06N3/04

Abstract: Systems and methods for searching a search space, specifically neural architecture search, are disclosed. Some examples may include using a first parallel module including a first plurality of stacked searching blocks and a second plurality of stacked searching blocks to output first feature maps of a first resolution and to output second feature maps of a second resolution. In some examples, a fusion module is configured to generate multiscale feature maps by fusing one or more feature maps of the first resolution received from the first parallel module with one or more feature maps of the second resolution received from the first parallel module, and wherein the fusion module is configured to output the multiscale feature maps and output third feature maps of a third resolution. The searching blocks can comprise a transformer with a projection function for learning self-attention in low-dimensional space.

公开(公告)号：WO2022260590A1

公开(公告)日：2022-12-15

申请号：PCT/SG2022/050295

申请日：2022-05-10

Applicant: LEMON INC.

Inventor： LIAN, Xiaochen ,  DING, Mingyu ,  YANG, Linjie ,  WANG, Peng ,  JIN, Xiaojie

IPC: G06N3/02 ,  G06V10/77 ,  G06V10/82

Abstract: Systems and methods for obtaining attention features are described. Some examples may include: receiving, at a projector of a transformer, a plurality of tokens associated with image features of a first dimensional space; generating, at the projector of the transformer, projected features by concatenating the plurality of tokens with a positional map, the projected features having a second dimensional space that is less than the first dimensional space; receiving, at an encoder of the transformer, the projected features and generating encoded representations of the projected features using self-attention; decoding, at a decoder of the transformer, the encoded representations and obtaining a decoded output; and projecting the decoded output to the first dimensional space and adding the image features of the first dimensional space to obtain attention features associated with the image features.