DETECTION TRANSFORMER FOR OBJECT DETECTION IN DIGITAL IMAGES BASED ON SUBGROUP PARTITIONING OF THE OBJECT SURVEY ARRANGEMENT

Abstract

Method for learning an object detection model in a digital image, said model being of the detection transformer type and comprising a first neural network (N1) adapted to determine features within the image, an encoder (NE) to generate information from these features, and a decoder comprising a self-attention layer (SL), the decoder being adapted to generate predictions (P1, P2... PN) from object queries (Q1, Q2... QN) based on information (Eo) provided by the encoder (NE), in which the queries are structured as subgroups (G1, G2... GK), the learning comprising a search for matching the predictions of each subgroup with the same training set.