Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟮𝟯𝟰 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗶𝗱𝘂𝗮𝗹 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻: A Simple but Effective Method for Multi-Label Recognition by Nanjing University, China Follow me for a similar post:  🇮🇳 Ashish Patel Interesting Facts : 🔸 This paper is published in ICCV2021with 8 citation. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/erQsB3Mc ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy computations, and lack of intuitive explanations. 🔸To effectively capture different spatial regions occupied by objects from different categories, we propose an embarrassingly simple module, named class-specific residual attention (CSRA). 🔸CSRA generates class-specific features for every category by proposing a simple spatial attention score, and then combines it with the class-agnostic average pooling feature. CSRA achieves state-of-the-art results on multilabel recognition, and at the 🔸same time is much simpler than them. Furthermore, with only 4 lines of code, CSRA also leads to consistent improvement across many diverse pretrained models and datasets without any extra training. 🔸CSRA is both easy to implement and light in computations, which also enjoys intuitive explanations and visualizations. #computervision #artificialintelligence #deeplearning #india