Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟭𝟲 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗼𝗳 𝗧𝗲𝗰𝗵𝗻𝗶𝘀𝗰𝗵𝗲 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁ä𝘁 𝗗𝗮𝗿𝗺𝘀𝘁𝗮𝗱𝘁, 𝗚𝗲𝗿𝗺𝗮𝗻𝘆 𝗵𝗮𝘀 𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗗𝗲𝗻𝘀𝗲𝗨𝗟𝗲𝗮𝗿𝗻:𝗗𝗲𝗻𝘀𝗲 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗩𝗶𝗱𝗲𝗼 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗗𝗲𝗻𝘀𝗲𝗨𝗟𝗲𝗮𝗿𝗻:𝗗𝗲𝗻𝘀𝗲 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗩𝗶𝗱𝗲𝗼 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 🔸 This paper is published in NeuroIPS 2021. 🔸 Unsupervised learning of visual representations has recently made considerable and expeditious advances, in part already outperforming even supervised feature learning methods. Most of these works, however, require substantial computational resources, and only a few accommodate one of the most ubiquitous types of visual data: videos.  🔸 In contrast to image sets, video data embeds ample information about typical transformations occurring in nature. Exploiting such cues may allow systems to learn more task-relevant invariances, instead of relying only on hand-engineered data augmentation. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 We present a novel approach to unsupervised learning for video object segmentation (VOS). Unlike previous work, our formulation allows learning dense feature representations directly in a fully convolutional regime.  🔸 We rely on uniform grid sampling to extract a set of anchors and train our model to disambiguate between them on both inter-and intra-video levels. However, a naive scheme to train such model results in a degenerate solution.  🔸 We propose to prevent this with a simple regularisation scheme, accommodating the equivariance property of the segmentation task to similarity transformations.  🔸 Our training objective admits efficient implementation and exhibits fast training convergence. On established VOS benchmarks, our approach exceeds the segmentation accuracy of previous work despite using significantly less training data and compute power. ------------------------------------------------------------------- #computervision #artificialintelligence #innovation -------------------------------------------------------------------