AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning, #MachineLearning, LLMs and #ComputerVision

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/

#artificialintelligence #machinelearning #ml #AI
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💄DEVIANT: SOTA in mono-3D detection💄

👉A novel Depth EquiVarIAnt NeTwork for 3D monocular detection in the wild

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Michigan + #Meta + Ford 🤯
Depth-equi. + scale equiv. steerable
New SOTA on KITTI & Waymo
Ok cross-dataset -> generalization

More: https://bit.ly/3OEFtgK
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🧱 Assembling #LEGO with #AI 🧱

👉Step-by-step assembly manual created by human into machine-interpretable instructions

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Stanford + MIT + #Google 🤯
MEPNet: Manual-to-Executable-Plan Net
Manual to machine-executable plan
2D manual - 3D geometric shape
Reasoning on 3D alignments of legos

More: https://bit.ly/3PCwn5C
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🎃New SOTA in UDA Semantic Seg.🎃

👉HRDA: multi-res Unsupervised Domain Adaptive Semantic Seg. -> SOTA

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
ETH + MPG + KU Leuven 🤯
HRDA: multi-res approach for UDA
Manageable GPU memory footprint
Small objects & fine segmentation detail
New SOTA on GTA and Synthia dataset

More: https://bit.ly/3cKtDEp
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⚗️ SemAbs: 3D Scene Understanding ⚗️

👉Framework that equips 2D Vision-Language Models (VLMs) with new 3D spatial capabilities

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
2D VLMs with 3D reasoning skills
ViTs Efficient MS Relevancy Extraction
Novel Open-World understanding tasks
Completing partially observed objects
Finding hidden objects from language

More: https://bit.ly/3PYYk7d
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🦚 TinyCD: Neural Change Detection 🦚

👉TinyCD: new SOTA in change detection with up to 150x fewer parameters.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
SOTA with up to 150X fewer params
Mixing blocks for s.t. cross-correlation
PW-MLP for pixel wise classification
MAMB: novel block for skip connection

More: https://bit.ly/3zFEngk
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🦊 3D-Aware "StyleGANv2" version 🦊

👉Upgrading StyleGANv2 into a novel 3D-aware GAN with just a minimal set of changes🤯

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
MPI-like 3D-aware GAN w/ single-view
GMPI: generative multiplane image
2D GAN 3D-aware with a minimal changes
Encoding 3D-aware inductive biases

More: https://bit.ly/3OJ5gnS
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📺 NeRF-ing "The Big Bang Theory" 📺

👉Berkeley unveils an approach for accurate estimation of actor’s 3D pose & location

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Input: images across the whole season
3D context (i.e. cams, structure, body)
Integrating context in 3D estimation
Re-ID, gaze, cinematography, pic editing
Knock, Knock, Penny!

More: https://bit.ly/3OLuaUb
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🎩ShAPO: SOTA in object understanding🎩

👉Joint multi-object detection, #3D texture, 6D object pose & size estimation.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Disentangled shape & appearance
Efficient octree-based differentiable
Object-centric understanding pipeline
Detection, reconstruction , 6D & size
SOTA in reconstruction & pose est.

More: https://bit.ly/3oHN5EQ
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🏙️ CityNeRF: Neural Rendering of City Scenes 🏙️

👉Progressive NeRF model and training set on city-scenes

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
BungeeNeRF: novel progressive NeRF
Details on drastically varied scales
Growing with residual block structure
Inclusive multi-level data supervision

More: https://bit.ly/3cS9vk7
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🍦🍦 Rewriting Geometry of GAN 🍦🍦

👉Drive GAN synthesizing many unseen objects with the desired shape

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
User-friendly "warping" with geometry
Low-rank update to layer for editing
Latent augmentation based on style-mix
Endless objects with defined changes
Latent space interpolation, image editing

More: https://bit.ly/3zIfOj8
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🍏🍏 GAUDI: the Neural Architect 🍏🍏

👉Novel generative model for immersive 3D scenes from a moving camera

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Hundreds of thousands pics/scenes
Novel denoising optimization objective
New SOTA across multiple datasets
Un/conditional on images/text

More: https://bit.ly/3Bt65ye
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🚜NeDDF: the NeRF evolution!🚜

👉Novel 3D representation that reciprocally constrains distance & density fields

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
NeRF provides no distance
Extending for arbitrary density
Density via dist-field & gradient
Alleviating the instability

More: https://bit.ly/3Bte8LC
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🔥AND/OR: Composable Diffusion Models🔥

👉Novel neural compositional generation via Composable Diffusion Models

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
DM as energy-based models
Connecting diffusion models
Conjunction & negation, on top of DM
Zero-shot combinatorial generalization

More: https://bit.ly/3PYv1Cs
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🔥 MobileNeRF is out -> Pure Fire! 🔥

👉MobileNeRF is out: the mobile evolution of NeRF via textured polygons.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Same quality, 10x faster than SNeRG
Memory-- by storing surface textures
Integrated GPUs: less memory/power
Suitable for browser & viewer is HTML

More: https://bit.ly/3PUKPWy
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🧣NeRF for Outdoor Scene Relighting🧣

👉NeRF-OSR: the first neural radiance fields approach for outdoor scene relighting

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
NeRF-method for outdoor relighting
Simultaneous illumination/viewpoint
Control over shading, shadow, albedo
Self-Supervised training from outdoor
Dataset: 3240 viewpoints, 110+ times

More: https://bit.ly/3vBiH2G
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👩‍🦰 Real-Time Neural Hair 👩‍🦰

👉Accurate hair geometry & appearance from multi-pics

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
Bonn, CMU and Reality Labs
Photorealistic Real-Time render
HQ strand geometry/appearance
Novel scalp texture description
Intuitive manipulation of 3D hair

More: https://bit.ly/3vBiH2G
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🚀 #VR by NASA - 1985 🚀

👉Q: is #VR the technology that developed least in the last 40 years? 🤔

Let's talk: https://bit.ly/3JxDZ7i
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