Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟴𝟬 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗛𝗬𝗟𝗗𝗔: 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗛𝘆𝗯𝗿𝗶𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗟𝗶𝗗𝗔𝗥 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Follow me for a similar post: Ashish Patel  ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗛𝗬𝗟𝗗𝗔: 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗛𝘆𝗯𝗿𝗶𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗟𝗶𝗗𝗔𝗥 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 🔸 This paper is published #arxiv 2022. 🔸 HYLDA integrated end-to-end architecture, which has three basic stages:  1) Input preprocessing,  2) our image-to-image translation engine,  and 3) the task stage composed of LiDAR semantic segmentation networks.  ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 In this paper we address the problem of training a LiDAR semantic segmentation network using a fully-labeled source dataset and a target dataset that only has a small number of labels.  🔸 To this end, we develop a novel imageto-image translation engine, and couple it with a LiDAR semantic segmentation network, resulting in an integrated domain adaptation architecture we call HYLDA.  🔸 To train the system end-to-end, we adopt a diverse set of learning paradigms, including  1) self-supervision on a simple auxiliary reconstruction task,  2) semi-supervised training using a few available labeled target domain frames, and 3) unsupervised training on the fake translated images generated by the imageto-image translation stage, together with the labeled frames from the source domain.  🔸 In the latter case, the semantic segmentation network participates in the updating of the imageto-image translation engine.  🔸 We demonstrate experimentally that HYLDA effectively addresses the challenging problem of improving generalization on validation data from the target domain when only a few target labeled frames are available for training.  🔸 We perform an extensive evaluation where we compare HYLDA against strong baseline methods using two publicly available LiDAR semantic segmentation datasets. #computervision #artificialintelligence #innovation