Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟬𝟴 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition by Tongji University Follow me for a similar post: @Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸This paper is published arxiv2022. 🔸Propose a novel patch-based VPR method named Patch-NetVLAD+ which consists of a fine-tuning strategy and a weighted matching strategy. 🔸The fine-tuning strategy is used to make original NetVLAD more suitable for extracting patch-level descriptors. The weighted matching strategy is used to find patches in LSR and make these patches easy to match by assigning a large weight to them. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge. 🔸Existing VPR methods using global descriptors have difficulty capturing local specific regions (LSR) in the scene and are therefore prone to localization confusion in such scenarios. 🔸As a result, finding the LSR that are critical for location recognition becomes key. To address this challenge, we introduced Patch-NetVLAD+, which was inspired by patch-based VPR researches. 🔸Our method proposed a fine-tuning strategy with triplet loss to make NetVLAD suitable for extracting patch-level descriptors. 🔸Moreover, unlike existing methods that treat all patches in an image equally, our method extracts patches of LSR, which present less frequently throughout the dataset, and makes them play an important role in VPR by assigning proper weights to them. 🔸Experiments on Pittsburgh30k and Tokyo247 datasets show that our approach achieved up to 6.35\% performance improvement than existing patch-based methods. #computervision #artificialintelligence #data