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π΄ Geogram: geometric algos in C++ π΄
πNovel open-source programming library with (research) geometric algorithms in C++
ππ’π π‘π₯π’π π‘ππ¬:
β Geometry Processing from #INRIA
β 30+ papers from SIGGRAPH, etc.
β Grants: GOODSHAPE & VORPALINE
β Code (mostly C++) under BSD 3
More: https://bit.ly/3mhS4L7
πNovel open-source programming library with (research) geometric algorithms in C++
ππ’π π‘π₯π’π π‘ππ¬:
β Geometry Processing from #INRIA
β 30+ papers from SIGGRAPH, etc.
β Grants: GOODSHAPE & VORPALINE
β Code (mostly C++) under BSD 3
More: https://bit.ly/3mhS4L7
π₯6π3β€1
π Open Source Vision from #Apple π
πCVNets: open-source (not a joke) lib for neural vision.
ππ’π π‘π₯π’π π‘ππ¬:
β PyTorch-based neural lib. for vision
β Train 2β4Γ longer w/ augmentations
β Plug-and-play components for CV
β Source code under a custom license
More: https://bit.ly/39d1dSj
πCVNets: open-source (not a joke) lib for neural vision.
ππ’π π‘π₯π’π π‘ππ¬:
β PyTorch-based neural lib. for vision
β Train 2β4Γ longer w/ augmentations
β Plug-and-play components for CV
β Source code under a custom license
More: https://bit.ly/39d1dSj
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ππ»Neural Clips by #Nvidia: INSANE ππ»
πNeural generation with changes in camera viewpoint & content that arises over time π€―
ππ’π π‘π₯π’π π‘ππ¬:
β Novel hierarchical generator architecture
β Temp. receptive field + temporal embed.
β Multi-res. with super-resolution network
β SOTA in long clip with motion & changes
β Code, data & models in August 2022 ποΈ
More: https://bit.ly/3zroWsC
πNeural generation with changes in camera viewpoint & content that arises over time π€―
ππ’π π‘π₯π’π π‘ππ¬:
β Novel hierarchical generator architecture
β Temp. receptive field + temporal embed.
β Multi-res. with super-resolution network
β SOTA in long clip with motion & changes
β Code, data & models in August 2022 ποΈ
More: https://bit.ly/3zroWsC
π€―9π2β€1
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β½ Zero to #Messi with #deeplearning β½
πEA unveils a neural system to learn multiple soccer juggling skills π
ππ’π π‘π₯π’π π‘ππ¬:
β Learning difficult soccer juggling skills
β Layer-wise mixture-of-experts architecture
β Specialization arises naturally
β Adaptive random walk training strategy
More: https://bit.ly/3mwRaL2
πEA unveils a neural system to learn multiple soccer juggling skills π
ππ’π π‘π₯π’π π‘ππ¬:
β Learning difficult soccer juggling skills
β Layer-wise mixture-of-experts architecture
β Specialization arises naturally
β Adaptive random walk training strategy
More: https://bit.ly/3mwRaL2
π₯7π3
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ποΈ HumanNeRF: source code is out! ποΈ
πPausing the video at any frame and rendering the subject from arbitrary views!
ππ’π π‘π₯π’π π‘ππ¬:
β Synthesizing photorealistic humans
β Synthesizing details, ie. cloth & face
β Volumetric canonical T-pose
β Skeletal rigid/non-rigid decomposition
More: https://bit.ly/3NEkTNY
πPausing the video at any frame and rendering the subject from arbitrary views!
ππ’π π‘π₯π’π π‘ππ¬:
β Synthesizing photorealistic humans
β Synthesizing details, ie. cloth & face
β Volumetric canonical T-pose
β Skeletal rigid/non-rigid decomposition
More: https://bit.ly/3NEkTNY
π€―17π₯5π2
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π EG3D: source code is out! π
π#Nvidia just opened EG3D: real time multi-view faces w/ HQ #3D geometry!
ππ’π π‘π₯π’π π‘ππ¬:
β Tri-plane-based 3D GAN framework
β Pose-correlated attribute (expression)
β SOTA in uncond. 3D-aware synthesis
β Source code & models NOW available!
More: https://bit.ly/3aOfHs0
π#Nvidia just opened EG3D: real time multi-view faces w/ HQ #3D geometry!
ππ’π π‘π₯π’π π‘ππ¬:
β Tri-plane-based 3D GAN framework
β Pose-correlated attribute (expression)
β SOTA in uncond. 3D-aware synthesis
β Source code & models NOW available!
More: https://bit.ly/3aOfHs0
π₯7π€―6π4β€2
π₯One Millisecond Backbone. Fire!π₯
πMobileOne by #Apple: efficient mobile backbone with inference <1 ms on #iPhone12!
ππ’π π‘π₯π’π π‘ππ¬:
β 75.9% top-1 accuracy on ImageNet
β 38Γ faster than MobileFormer net
β Classification, detection & segmentation
β Source code & model soon available!
More: https://bit.ly/3tsT7f2
πMobileOne by #Apple: efficient mobile backbone with inference <1 ms on #iPhone12!
ππ’π π‘π₯π’π π‘ππ¬:
β 75.9% top-1 accuracy on ImageNet
β 38Γ faster than MobileFormer net
β Classification, detection & segmentation
β Source code & model soon available!
More: https://bit.ly/3tsT7f2
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𧨠Scaling Transformers to GigaPixels!π§¨
πNovel ViT called Hierarchical Image Pyramid Transformer (HIPT) -> Scaling to GigaPixels!
ππ’π π‘π₯π’π π‘ππ¬:
β Gigapixel whole-slide imaging (WSI)
β Leveraging natural hier. structure of WSI
β Self-supervised Hi-Res representations
β Source code and models available!
More: https://bit.ly/3xLuzkg
πNovel ViT called Hierarchical Image Pyramid Transformer (HIPT) -> Scaling to GigaPixels!
ππ’π π‘π₯π’π π‘ππ¬:
β Gigapixel whole-slide imaging (WSI)
β Leveraging natural hier. structure of WSI
β Self-supervised Hi-Res representations
β Source code and models available!
More: https://bit.ly/3xLuzkg
π€―16π1
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πBodyMap: Hyper-Detailed Humansπ
π#META unveils 1st-ever dense continuous correspondence for clothed humans
ππ’π π‘π₯π’π π‘ππ¬:
β 1st-ever dense continuous corresp.
β HQ fingers, hair, and clothes
β Novel ViT-based architecture
β SOTA on DensePose COCO
More: https://bit.ly/39nEPps
π#META unveils 1st-ever dense continuous correspondence for clothed humans
ππ’π π‘π₯π’π π‘ππ¬:
β 1st-ever dense continuous corresp.
β HQ fingers, hair, and clothes
β Novel ViT-based architecture
β SOTA on DensePose COCO
More: https://bit.ly/39nEPps
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πΉ NOAH just open-sourced! πΉ
πA novel approach to find the optimal design of prompt modules through NAS algos.
ππ’π π‘π₯π’π π‘ππ¬:
β NOAH from Neural prOmpt seArcH
β Parameter-efficient βprompt modulesβ
β Efficient NAS-based implementation
β Better than transfer, few-shot & domain gen.
More: https://bit.ly/3MKfVhi
πA novel approach to find the optimal design of prompt modules through NAS algos.
ππ’π π‘π₯π’π π‘ππ¬:
β NOAH from Neural prOmpt seArcH
β Parameter-efficient βprompt modulesβ
β Efficient NAS-based implementation
β Better than transfer, few-shot & domain gen.
More: https://bit.ly/3MKfVhi
π5π2π₯°1
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ππ»ββοΈNeural Super-Resolution in Moviesππ»ββοΈ
πImplicit neural representation to get arbitrary spatial resolution & FPS -> Super Resolution!
ππ’π π‘π₯π’π π‘ππ¬:
β Video as continuous video representation
β Clips in arbitrary space/time resolution
β OOD generalization in space-time
β Source code and models available
More: https://bit.ly/3xsqccf
πImplicit neural representation to get arbitrary spatial resolution & FPS -> Super Resolution!
ππ’π π‘π₯π’π π‘ππ¬:
β Video as continuous video representation
β Clips in arbitrary space/time resolution
β OOD generalization in space-time
β Source code and models available
More: https://bit.ly/3xsqccf
π₯6π2
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π§ Bias in #AI, explained simple π§
πAsking DallE-Mini to help me to show what the BIAS in #AI is
πππ§ππ«ππππ πππ¦π©π₯ππ¬:
β Best eng.->men/Caucasians
β Best doctors->men/Caucasians
β Top CEOs->men/Caucasians
β Chef, kitchen->men/Caucasians
β Rich People->only Caucasians
β Poor People->non-Caucasians
β Italian engineers->back in 30's
β Chinese eng.->infrastructures
β Italian working->local market
β Chinese working->vegetables
β Men workers->constructions
β Women workers->only office
More: https://bit.ly/3b0UFqd
πAsking DallE-Mini to help me to show what the BIAS in #AI is
πππ§ππ«ππππ πππ¦π©π₯ππ¬:
β Best eng.->men/Caucasians
β Best doctors->men/Caucasians
β Top CEOs->men/Caucasians
β Chef, kitchen->men/Caucasians
β Rich People->only Caucasians
β Poor People->non-Caucasians
β Italian engineers->back in 30's
β Chinese eng.->infrastructures
β Italian working->local market
β Chinese working->vegetables
β Men workers->constructions
β Women workers->only office
More: https://bit.ly/3b0UFqd
π13β€6π4
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π¦ SAVi++: Segmentation by #Google π¦
πNovel unsupervised object-centric #AI to predict depth signals from slot-based video representation
ππ’π π‘π₯π’π π‘ππ¬:
β Segmenting complex dynamic scenes
β Static/Moving objects on naturalistic BG
β LiDAR-SAVi: segmenting in the wild
β Source code and model soon available!
More: https://bit.ly/3n3hywd
πNovel unsupervised object-centric #AI to predict depth signals from slot-based video representation
ππ’π π‘π₯π’π π‘ππ¬:
β Segmenting complex dynamic scenes
β Static/Moving objects on naturalistic BG
β LiDAR-SAVi: segmenting in the wild
β Source code and model soon available!
More: https://bit.ly/3n3hywd
π₯7π6π₯°1
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βHaGRID : Half Million Handsπ
πRussian Sberbank opens HaGRID, enormous dataset for HGR. "Peace" label is present π΅π‘
ππ’π π‘π₯π’π π‘ππ¬:
β 552,992 samples, 18 classes
β HD resolution in RGB format
β BBox, gesture, leading hands
β Dataset/models available
More: https://bit.ly/3n2cd8r
πRussian Sberbank opens HaGRID, enormous dataset for HGR. "Peace" label is present π΅π‘
ππ’π π‘π₯π’π π‘ππ¬:
β 552,992 samples, 18 classes
β HD resolution in RGB format
β BBox, gesture, leading hands
β Dataset/models available
More: https://bit.ly/3n2cd8r
β€11π€2
π₯ #AIwithPapers: we are 2,900+! π₯
ππ Cheers from "Black Metal Lady Gaga" plotted by DallE-mini ππ
π Invite your friends -> https://t.me/AI_DeepLearning
ππ Cheers from "Black Metal Lady Gaga" plotted by DallE-mini ππ
π Invite your friends -> https://t.me/AI_DeepLearning
π8π3β€2
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π
Segmentation with INSANE Occlusionsπ
πCMU unveils WALT: segmenting in severe occlusion scenarios. Performance over human.
ππ’π π‘π₯π’π π‘ππ¬:
β WALT: Watch & Learn Time-lapse
β 4K/1080p cams on streets over a year
β Performance over human-supervised
β Object-occluder-occluded neural layers
β Source code under MIT license
More: https://bit.ly/3n7pvjO
πCMU unveils WALT: segmenting in severe occlusion scenarios. Performance over human.
ππ’π π‘π₯π’π π‘ππ¬:
β WALT: Watch & Learn Time-lapse
β 4K/1080p cams on streets over a year
β Performance over human-supervised
β Object-occluder-occluded neural layers
β Source code under MIT license
More: https://bit.ly/3n7pvjO
π€―14π4π₯3
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π Largest Dataset for #autonomousdrivingπ
πSHIFT: largest synthetic dataset for #selfdrivingcars. Shifts in cloud, rain, fog, time of day, vehicle & pedestrian densityπ€―
ππ’π π‘π₯π’π π‘ππ¬:
β 4,800+ clips, multi-view sensor suite
β Semantic/instance, M/stereo depth
β 2D/3D object detection, MOT
β Optical flow, point cloud registration
β Visual-Odo, trajectory & human pose
More: https://bit.ly/3HJBUUT
πSHIFT: largest synthetic dataset for #selfdrivingcars. Shifts in cloud, rain, fog, time of day, vehicle & pedestrian densityπ€―
ππ’π π‘π₯π’π π‘ππ¬:
β 4,800+ clips, multi-view sensor suite
β Semantic/instance, M/stereo depth
β 2D/3D object detection, MOT
β Optical flow, point cloud registration
β Visual-Odo, trajectory & human pose
More: https://bit.ly/3HJBUUT
π€―9π5β€2
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π¦Big Egocentric Dataset by #Meta π¦
πNovel dataset to speed-up research on egocentric MR/AI
ππ’π π‘π₯π’π π‘ππ¬:
β 159 sequences, multiple sensors
β Scenarios: cooking, exercising, etc.
β βDesktop Activitiesβ via multi-view mocap
β Dataset available upon request
More: https://bit.ly/3QDccVW
πNovel dataset to speed-up research on egocentric MR/AI
ππ’π π‘π₯π’π π‘ππ¬:
β 159 sequences, multiple sensors
β Scenarios: cooking, exercising, etc.
β βDesktop Activitiesβ via multi-view mocap
β Dataset available upon request
More: https://bit.ly/3QDccVW
π₯8π3
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π¦Transf-Codebook HD-Face Restorationπ¦
πS-Lab unveils CodeFormer: hyper-datailed face restoration from degraded clips
ππ’π π‘π₯π’π π‘ππ¬:
β Face restoration as a code prediction
β Discrete CB prior in small proxy space
β Controllable transformation for LQ->HQ
β Robustness and global coherence
β Code and models soon available
More: https://bit.ly/3QEa9B5
πS-Lab unveils CodeFormer: hyper-datailed face restoration from degraded clips
ππ’π π‘π₯π’π π‘ππ¬:
β Face restoration as a code prediction
β Discrete CB prior in small proxy space
β Controllable transformation for LQ->HQ
β Robustness and global coherence
β Code and models soon available
More: https://bit.ly/3QEa9B5
π₯13π7β€1