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๐ฉโ๐ฆฐBack in the 50's with GAN๐ฉโ๐ฆฐ
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ A few thousand vintage faces
โ Models available for download
โ Stylegan2-ffhqu-1024x1024
โ NO Commercial allowed
More: https://bit.ly/3LlOyKX
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ A few thousand vintage faces
โ Models available for download
โ Stylegan2-ffhqu-1024x1024
โ NO Commercial allowed
More: https://bit.ly/3LlOyKX
๐คฏ2โค1๐ฑ1
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๐ฆ VNCA: bio-inspired generative model ๐ฆ
๐A novel generative model loosely inspired by the biological processes of cellular growth and differentiation
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Variational Neural Cellular Automata
โ Probabilistic generative model
โ Learn from common vector format
โ Learn purely s.o. generative process
โ Far away from SOTA, but interesting
More: https://bit.ly/3oGb2wG
๐A novel generative model loosely inspired by the biological processes of cellular growth and differentiation
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Variational Neural Cellular Automata
โ Probabilistic generative model
โ Learn from common vector format
โ Learn purely s.o. generative process
โ Far away from SOTA, but interesting
More: https://bit.ly/3oGb2wG
๐4๐ฅ1๐คฏ1
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๐Block-NeRF: Neural View Synthesis๐
๐Large-scale scene reconstruction by multiple compact NeRFs that each fit into memory.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Berkeley + Google + Waymo = ๐คฏ
โ Scaling NeRF to city-scale scenes
โ Trick: multiple simple NeRFs
โ Time decoupled, arbitrarily large scene
โ Data over months & different conditions
More: https://bit.ly/3GGVHBV
๐Large-scale scene reconstruction by multiple compact NeRFs that each fit into memory.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Berkeley + Google + Waymo = ๐คฏ
โ Scaling NeRF to city-scale scenes
โ Trick: multiple simple NeRFs
โ Time decoupled, arbitrarily large scene
โ Data over months & different conditions
More: https://bit.ly/3GGVHBV
๐4๐ฅ3๐คฏ1
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๐ฅฌHW-Accelerated Neuro-Evolution๐ฅฌ
๐Scalable, general purpose, hardware accelerated neuro-evolution toolkit by Google
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Parallel on multiple TPU/GPUs
โ Neuro-evo algorithms with NNs
โ WaterWorld, Abstract paint, more
โ From Google, not an official product
โ Code under Apache License 2.0
More: https://bit.ly/3szEi9w
๐Scalable, general purpose, hardware accelerated neuro-evolution toolkit by Google
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Parallel on multiple TPU/GPUs
โ Neuro-evo algorithms with NNs
โ WaterWorld, Abstract paint, more
โ From Google, not an official product
โ Code under Apache License 2.0
More: https://bit.ly/3szEi9w
๐3๐ฅ2๐คฏ1๐ฑ1
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๐ DeepETA: #Uber ETA via #AI๐
๐Uber unveils the low-latency deep architecture for global ETA prediction
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Latency / Accuracy / Generality
โ 7 NNs architectures tested
โ Encoder-decoder + Self-Attention
โ Linear transformer (kernel trick)
โ Feature sparsity for speed
More: https://bit.ly/3gFWmJh
๐Uber unveils the low-latency deep architecture for global ETA prediction
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Latency / Accuracy / Generality
โ 7 NNs architectures tested
โ Encoder-decoder + Self-Attention
โ Linear transformer (kernel trick)
โ Feature sparsity for speed
More: https://bit.ly/3gFWmJh
๐3๐ฅ1๐คฏ1
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โ๏ธCLIPasso: Semantic Sketching via CLIPโ๏ธ
๐Sketching method guided by geometric and semantic simplifications (CLIP)
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ EPFL, TAU and IDC Herzliya
โ CLIP image encoder for sketching
โ Sketching as a set of Bezier curves
โ Param-optimization on CLIP-loss
โ Source code and models available
More: https://bit.ly/3oLEDF4
๐Sketching method guided by geometric and semantic simplifications (CLIP)
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ EPFL, TAU and IDC Herzliya
โ CLIP image encoder for sketching
โ Sketching as a set of Bezier curves
โ Param-optimization on CLIP-loss
โ Source code and models available
More: https://bit.ly/3oLEDF4
๐ฅ2๐ฅฐ2๐1
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๐ชSAHI: slicing detection/segmentation๐ช
๐An open-source lightweight library for large scale object detection & instance segmentation
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Slicing Aided Hyper Inference
โ Large-scale detection/segment.
โ Sliced inference and merging
โ Utils for conversion, slicing, etc.
โ Code licensed under MIT License
More: https://bit.ly/3uMJoBZ
๐An open-source lightweight library for large scale object detection & instance segmentation
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Slicing Aided Hyper Inference
โ Large-scale detection/segment.
โ Sliced inference and merging
โ Utils for conversion, slicing, etc.
โ Code licensed under MIT License
More: https://bit.ly/3uMJoBZ
๐ฅ3โค2๐คฏ1
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๐100,000,000 image-text pairs!๐
๐Large-scale Chinese cross-modal dataset for benchmarking different multi-modal pre-training methods.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ 100 Million <image, text> pairs
โ >200px size, aspect ratio (1/3~3)
โ Models of ResNet, ViT & SwinT
โ Methods: CLIP, FILIP and LiT
โ Privacy/Sensitive words ๐ค
More: https://bit.ly/34BqlzX
๐Large-scale Chinese cross-modal dataset for benchmarking different multi-modal pre-training methods.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ 100 Million <image, text> pairs
โ >200px size, aspect ratio (1/3~3)
โ Models of ResNet, ViT & SwinT
โ Methods: CLIP, FILIP and LiT
โ Privacy/Sensitive words ๐ค
More: https://bit.ly/34BqlzX
๐5๐ค1
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๐ง33 Million synthetic pedestrians๐ง
๐A novel large, fully synthetic dataset
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Exploiting the #gta5 engine
โ 764 full-HD videos @20 fps
โ 33M+ person instances
โ BBs & segmentation masks
โ 2D/3D keypoints & depth
More: https://bit.ly/36njlY1
๐A novel large, fully synthetic dataset
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Exploiting the #gta5 engine
โ 764 full-HD videos @20 fps
โ 33M+ person instances
โ BBs & segmentation masks
โ 2D/3D keypoints & depth
More: https://bit.ly/36njlY1
๐6๐คฏ1
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๐ฅMarker-free 6D-point tracking๐ฅ
๐Full position and rotation of skeletal joints, with only a RGB frame
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Full 3-axis joint rotations
โ V-markers, emulating mocap
โ #3D from monocular with NN
โ Generalization, no retraining
โ SOTA rotation/position est.
More: https://bit.ly/34GdoF5
๐Full position and rotation of skeletal joints, with only a RGB frame
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Full 3-axis joint rotations
โ V-markers, emulating mocap
โ #3D from monocular with NN
โ Generalization, no retraining
โ SOTA rotation/position est.
More: https://bit.ly/34GdoF5
๐ฅ12๐คฏ1
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๐งผ Synthetic dataset for #Retail ๐งผ
๐A large-scale photorealistic synthetic dataset with annotations for semantic segmentation, instance segmentation, depth estimation, and object detection.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Dataset from Standard.AI
โ 2,134 unique scenes
โ 25k+ annotated samples
โ Introducing the "change detection"
โ Multi-view representation learning
โ NonCommercial-ShareAlike 4.0
More: https://bit.ly/3uXqubB
๐A large-scale photorealistic synthetic dataset with annotations for semantic segmentation, instance segmentation, depth estimation, and object detection.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Dataset from Standard.AI
โ 2,134 unique scenes
โ 25k+ annotated samples
โ Introducing the "change detection"
โ Multi-view representation learning
โ NonCommercial-ShareAlike 4.0
More: https://bit.ly/3uXqubB
๐คฏ6๐ฅฐ3๐1๐ฅ1๐1
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๐ Graph Neural Nets Forecasting๐
๐Data-driven approach for forecasting global weather using graph neural networks
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Data-driven forecasting via GNNs
โ Model: 6.7M parameters, float32
โ 6-hours forecast in 0.04 secs.
โ A 5-day forecast in 0.8 secs.
More: https://bit.ly/3LH4CXR
๐Data-driven approach for forecasting global weather using graph neural networks
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Data-driven forecasting via GNNs
โ Model: 6.7M parameters, float32
โ 6-hours forecast in 0.04 secs.
โ A 5-day forecast in 0.8 secs.
More: https://bit.ly/3LH4CXR
๐4๐2๐ค1
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๐ฅซWatch Those Words!๐ฅซ
๐Berkeley unveils a novel approach to discover cheap-fake and visually persuasive deep-fakes
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Regardless of falsification
โ Semantic person-specific
โ Word-conditioned analysis
โ Generalization across fakes
More: https://bit.ly/3oXWmcd
๐Berkeley unveils a novel approach to discover cheap-fake and visually persuasive deep-fakes
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Regardless of falsification
โ Semantic person-specific
โ Word-conditioned analysis
โ Generalization across fakes
More: https://bit.ly/3oXWmcd
๐5๐ฑ1
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๐V2X-sim for #selfdriving is out!๐
๐V2X: collaboration between a vehicle and any surrounding entity
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Suitable for #selfdrivingcars
โ Rec. from road & vehicles
โ Multi-streams/perception
โ Detection, tracking, & segmentation
โ RGB, depth, semantic, BEV & LiDAR
More: https://bit.ly/3H6veOI
๐V2X: collaboration between a vehicle and any surrounding entity
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Suitable for #selfdrivingcars
โ Rec. from road & vehicles
โ Multi-streams/perception
โ Detection, tracking, & segmentation
โ RGB, depth, semantic, BEV & LiDAR
More: https://bit.ly/3H6veOI
๐ฅ6๐คฉ1
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๐Infinite Synthetic dataset for Fitness๐
๐Opensource synthetic images for fitness, single/multi-person, and realistic variation in lighting, camera angles, and occlusions
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ 60k images, 1-5 avatars
โ 15 categories, 21 variations
โ Blender and ray-tracing
โ SMPL-X + facial expression
โ Cloth/skin tone sampled
โ 147 4K HDRI panoramas
โ Creative Commons 4.0
More: https://bit.ly/33B1R9q
๐Opensource synthetic images for fitness, single/multi-person, and realistic variation in lighting, camera angles, and occlusions
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ 60k images, 1-5 avatars
โ 15 categories, 21 variations
โ Blender and ray-tracing
โ SMPL-X + facial expression
โ Cloth/skin tone sampled
โ 147 4K HDRI panoramas
โ Creative Commons 4.0
More: https://bit.ly/33B1R9q
๐คฉ5โค1๐1
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โ DITTO: Digital Twins from Interaction โ
๐Digitizing objects for #metaverse through interactive perception
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ DIgital Twin of arTiculated Objects
โ Geometry & kinematic articulation
โ Articulation & 3D via perception
โ Source code under MIT License
More:https://bit.ly/3LMazCV
๐Digitizing objects for #metaverse through interactive perception
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ DIgital Twin of arTiculated Objects
โ Geometry & kinematic articulation
โ Articulation & 3D via perception
โ Source code under MIT License
More:https://bit.ly/3LMazCV
๐ฅ5โค2๐1๐คฏ1
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๐ค Robotic Telekinesis from Youtube ๐ค
๐CMU unveils a Robot that observes humans and imitates their actions in real-time
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Enabling robo-hand teleoperation
โ Suitable for untrained operator
โ Single uncalibrated RGB camera
โ Leveraging unlabeled #youtube
โ No active fine-tuning or setup
โ No collision via Adv-Training
More: https://bit.ly/3H7zUnh
๐CMU unveils a Robot that observes humans and imitates their actions in real-time
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Enabling robo-hand teleoperation
โ Suitable for untrained operator
โ Single uncalibrated RGB camera
โ Leveraging unlabeled #youtube
โ No active fine-tuning or setup
โ No collision via Adv-Training
More: https://bit.ly/3H7zUnh
๐ฅ3๐คฏ2๐1๐1
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๐DIGAN: #AI for video generation๐
๐A novel INR-based generative adversarial network for video generation
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Dynamics-aware generator
โ INR-based clip generator
โ Manipulating space/time
โ Identifying unnatural motion
More: https://bit.ly/3H6sHE4
๐A novel INR-based generative adversarial network for video generation
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Dynamics-aware generator
โ INR-based clip generator
โ Manipulating space/time
โ Identifying unnatural motion
More: https://bit.ly/3H6sHE4
๐ฅ4๐คฏ1
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๐ฆFILM Neural Frame Interpolation๐ฆ
๐Frame interpolation that synthesizes multiple intermediate frames from two input images with large in-between motion
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Single unified network
โ High quality output
โ SOTA on the Xiph
โ Apache License 2.0
More: https://bit.ly/3pl4ZxH
๐Frame interpolation that synthesizes multiple intermediate frames from two input images with large in-between motion
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Single unified network
โ High quality output
โ SOTA on the Xiph
โ Apache License 2.0
More: https://bit.ly/3pl4ZxH
๐ฅ5๐2๐ฅฐ1
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๐Neural Maintenance via listening๐
๐Novel neural-method to detect whether a machine is "healthy" or requires maintenance
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Defects at an early stage
โ FDWT, fast discrete wavelet
โ Learnable wavelet/denoising
โ Unsupervised learnable FDWT
โ The new SOTA in PM
More: https://bit.ly/3hiKWeX
๐Novel neural-method to detect whether a machine is "healthy" or requires maintenance
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Defects at an early stage
โ FDWT, fast discrete wavelet
โ Learnable wavelet/denoising
โ Unsupervised learnable FDWT
โ The new SOTA in PM
More: https://bit.ly/3hiKWeX
๐คฏ6๐ค1