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🐲A novel AI-controllable synthesis🐲
👉Modeling local semantic parts separately and synthesizing images in a compositional way
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Structure & texture locally controlled
✅Disentanglement between areas
✅Fine-grained editing of images
✅Extendible via transfer learning
✅Just accepted to #CVPR2022
More: https://bit.ly/3IBgkBy
👉Modeling local semantic parts separately and synthesizing images in a compositional way
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Structure & texture locally controlled
✅Disentanglement between areas
✅Fine-grained editing of images
✅Extendible via transfer learning
✅Just accepted to #CVPR2022
More: https://bit.ly/3IBgkBy
😱3🤯2❤1
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🥶 E2V-SDE: biggest troll ever? 🥶
👉E2V-SDE paper (accepted to #CVPR2022) consists of texts copied from 10+ previously published papers 😂
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latent ODEs for Irregularly-Sampled TS
✅Stochastic Adversarial Video Prediction
✅Continuous Latent Process Flows
✅More papers....
More: https://bit.ly/3bsL8Zw (AUDIO ON!)
👉E2V-SDE paper (accepted to #CVPR2022) consists of texts copied from 10+ previously published papers 😂
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latent ODEs for Irregularly-Sampled TS
✅Stochastic Adversarial Video Prediction
✅Continuous Latent Process Flows
✅More papers....
More: https://bit.ly/3bsL8Zw (AUDIO ON!)
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🔥🔥MultiNeRF: three NeRFs are out!🔥🔥
👉Google opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Paper_1: Mip-NeRF 360
✅Paper_2: Ref-NeRF
✅Paper_3: NeRF in the Dark
More: https://bit.ly/3QjpRRc
👉Google opens the code of three #cvpr2022 papers: Mip-NeRF 360, Ref-NeRF, RawNeRF
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Paper_1: Mip-NeRF 360
✅Paper_2: Ref-NeRF
✅Paper_3: NeRF in the Dark
More: https://bit.ly/3QjpRRc
👍13❤4🤯4
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🔥IDOL (#CVPR2022 winner): code is out!🔥
👉IDOL for VIS: outperforming all online/offline methods, the new SOTA!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Online usually inferior by >10AP
✅Online based on contrast-learning
✅Discriminative++ instance embeddings
✅Full exploiting history for stability
More https://bit.ly/3dXCDXw
👉IDOL for VIS: outperforming all online/offline methods, the new SOTA!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Online usually inferior by >10AP
✅Online based on contrast-learning
✅Discriminative++ instance embeddings
✅Full exploiting history for stability
More https://bit.ly/3dXCDXw
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