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🔥 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |

𝗗𝗮𝘆-𝟭𝟱𝟭Computer Vision Learning 𝗠𝗖𝗡: Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation by Xidian University, China Follow me for similar post :  🇮🇳 Ashish Patel Interesting Facts : 🔸 This is a paper in CVPR 2020 with over 14 citations. 🔸 It Outperforms with the MMI(VGG16), CMN(frcnn-resnet101), Spe+Lis+RI(frcnn-vgg16), ParalAttn(frcnn-vgg16), LGRANs(frcnn-vgg16), NMTree(frcnn-vgg16), FAOA(Darknet63), MattNet(mrcnn-resnet101) etc. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/e8dbff5 code : https://lnkd.in/eAuuZZX ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Referring expression comprehension (REC) and segmentation (RES) are two highly related tasks, which both aim at identifying the referent according to a natural language expression. 🔸 In MCN, RES can help REC to achieve better language-vision alignment, while REC can help RES to better locate the referent. In addition, we address a key challenge in this multi-task setup, i.e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS). Specifically, CEM enables REC and RES to focus on similar visual regions by maximizing the consistency energy between two tasks.  #computervision #artificialintelligence #innovation

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Ashish Patel 🇮🇳

🔥 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |

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