🦕JoJoGAN: One Shot Face Stylization🦕

👉UIUC researchers unveil a novel method for one-shot image stylization.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Stylization from single input
✅Finetuning StyleGAN for stylization
✅No supervision, good generalization
✅MIT License (commercial allowed)

More: https://bit.ly/3ASVzyb

🧦SOTA in OOD detection for safer #AI🧦

👉Out-of-distribution (OOD) detection produces wrong/overconfident predictions.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel framework for OOD
✅Synthesizing virtual outliers
✅Novel unknown-aware training
✅Code and model available

More: https://bit.ly/3JnFIL9

🌅StyleGAN-XL neural synthesis🌅

👉From Tübingen, StyleGAN-XL: new SOTA for large diverse dataset.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅First 1024p-gen for large data
✅Growing strategy on StyleGAN3
✅Beyond the narrow domains
✅Pivotal Tuning Inversion (TPI)
✅SOTA vs. GAN & diffusion models

More: https://bit.ly/3HK9MQk

📌This keypoint is pure GLUE📌

👉Keypoints play a central role in computer vision.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel Object-centric keypoint
✅Novel sim2real training method
✅Intra-salience / inter-distinctness
✅Enforcing semantic consistency
✅Close to fully-supervised method!

More: https://bit.ly/3rth1qh

💡 LEDNet: seeing in the dark 💡

👉Researchers from NTU unveil LEDNet to see in the dark

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel data synthesis for low-light
✅Low-light/deblurring dataset
✅12k low-blur/normal-sharp pairs
✅LEDNet: lowlight + deblurring


More: https://bit.ly/3HIyYqM

👩‍🦰Back in the 50's with GAN👩‍🦰

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅A few thousand vintage faces
✅Models available for download
✅Stylegan2-ffhqu-1024x1024
✅NO Commercial allowed

More: https://bit.ly/3LlOyKX

🦠VNCA: bio-inspired generative model 🦠

👉A novel generative model loosely inspired by the biological processes of cellular growth and differentiation

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Variational Neural Cellular Automata
✅Probabilistic generative model
✅Learn from common vector format
✅Learn purely s.o. generative process
✅Far away from SOTA, but interesting

More: https://bit.ly/3oGb2wG

🍊Block-NeRF: Neural View Synthesis🍊

👉Large-scale scene reconstruction by multiple compact NeRFs that each fit into memory.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Berkeley + Google + Waymo = 🤯
✅Scaling NeRF to city-scale scenes
✅Trick: multiple simple NeRFs
✅Time decoupled, arbitrarily large scene
✅Data over months & different conditions

More: https://bit.ly/3GGVHBV

🥬HW-Accelerated Neuro-Evolution🥬

👉Scalable, general purpose, hardware accelerated neuro-evolution toolkit by Google

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Parallel on multiple TPU/GPUs
✅Neuro-evo algorithms with NNs
✅WaterWorld, Abstract paint, more
✅From Google, not an official product
✅Code under Apache License 2.0

More: https://bit.ly/3szEi9w

🚛 DeepETA: #Uber ETA via #AI🚛

👉Uber unveils the low-latency deep architecture for global ETA prediction

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latency / Accuracy / Generality
✅7 NNs architectures tested
✅Encoder-decoder + Self-Attention
✅Linear transformer (kernel trick)
✅Feature sparsity for speed

More: https://bit.ly/3gFWmJh

✏️CLIPasso: Semantic Sketching via CLIP✏️

👉Sketching method guided by geometric and semantic simplifications (CLIP)

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅EPFL, TAU and IDC Herzliya
✅CLIP image encoder for sketching
✅Sketching as a set of Bezier curves
✅Param-optimization on CLIP-loss
✅Source code and models available

More: https://bit.ly/3oLEDF4

🪂SAHI: slicing detection/segmentation🪂

👉An open-source lightweight library for large scale object detection & instance segmentation

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Slicing Aided Hyper Inference
✅Large-scale detection/segment.
✅Sliced inference and merging
✅Utils for conversion, slicing, etc.
✅Code licensed under MIT License

More: https://bit.ly/3uMJoBZ

🎁100,000,000 image-text pairs!🎁

👉Large-scale Chinese cross-modal dataset for benchmarking different multi-modal pre-training methods.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅100 Million <image, text> pairs
✅>200px size, aspect ratio (1/3~3)
✅Models of ResNet, ViT & SwinT
✅Methods: CLIP, FILIP and LiT
✅Privacy/Sensitive words 🤔

More: https://bit.ly/34BqlzX

🧁33 Million synthetic pedestrians🧁

👉A novel large, fully synthetic dataset

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Exploiting the #gta5 engine
✅764 full-HD videos @20 fps
✅33M+ person instances
✅BBs & segmentation masks
✅2D/3D keypoints & depth

More: https://bit.ly/36njlY1

🥝Marker-free 6D-point tracking🥝

👉Full position and rotation of skeletal joints, with only a RGB frame

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Full 3-axis joint rotations
✅V-markers, emulating mocap
✅#3D from monocular with NN
✅Generalization, no retraining
✅SOTA rotation/position est.

More: https://bit.ly/34GdoF5

🧼 Synthetic dataset for #Retail 🧼

👉A large-scale photorealistic synthetic dataset with annotations for semantic segmentation, instance segmentation, depth estimation, and object detection.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Dataset from Standard.AI
✅2,134 unique scenes
✅25k+ annotated samples
✅Introducing the "change detection"
✅Multi-view representation learning
✅NonCommercial-ShareAlike 4.0

More: https://bit.ly/3uXqubB

🌈 Graph Neural Nets Forecasting🌈

👉Data-driven approach for forecasting global weather using graph neural networks

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Data-driven forecasting via GNNs
✅Model: 6.7M parameters, float32
✅6-hours forecast in 0.04 secs.
✅A 5-day forecast in 0.8 secs.

More: https://bit.ly/3LH4CXR

🥫Watch Those Words!🥫

👉Berkeley unveils a novel approach to discover cheap-fake and visually persuasive deep-fakes

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Regardless of falsification
✅Semantic person-specific
✅Word-conditioned analysis
✅Generalization across fakes

More: https://bit.ly/3oXWmcd

🔋V2X-sim for #selfdriving is out!🔋

👉V2X: collaboration between a vehicle and any surrounding entity

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Suitable for #selfdrivingcars
✅Rec. from road & vehicles
✅Multi-streams/perception
✅Detection, tracking, & segmentation
✅RGB, depth, semantic, BEV & LiDAR

More: https://bit.ly/3H6veOI

🍏Infinite Synthetic dataset for Fitness🍏

👉Opensource synthetic images for fitness, single/multi-person, and realistic variation in lighting, camera angles, and occlusions

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅60k images, 1-5 avatars
✅15 categories, 21 variations
✅Blender and ray-tracing
✅SMPL-X + facial expression
✅Cloth/skin tone sampled
✅147 4K HDRI panoramas
✅Creative Commons 4.0

More: https://bit.ly/33B1R9q