AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates on Deep Learning, Machine Learning, and Computer Vision (with Papers).

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/
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πŸƒReplaceAnything: demo is out!πŸƒ

πŸ‘‰ReplaceAnything: ultra-high quality content replacement. The ultimate #AI solution for human, clothing & background replacement to change the e-commerce experience for vendors.

πŸ‘‰Review https://t.ly/FMyvf
πŸ‘‰Project https://lnkd.in/dcyZvP2b
πŸ‘‰ModelScope https://lnkd.in/dU4x4nE6
πŸ‘‰Hugging Face https://lnkd.in/dn3uXWgd
πŸ‘‰Empty report https://lnkd.in/dcuGXd6c
πŸ‘‰Paper coming?
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πŸ₯› Transparent Object Tracking πŸ₯›

πŸ‘‰Trans2k: transparent object tracking dataset of 2,000+ sequences with 100,000+ images, annotated by bounding boxes & segmentation mask.

πŸ‘‰Review https://t.ly/mEI6O
πŸ‘‰Paper https://lnkd.in/dsudY3DB
πŸ‘‰Project https://lnkd.in/d48SSJJ3
πŸ‘‰TOB https://lnkd.in/dykBUNfC
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πŸ’ŠπŸ’Š AGNOSTIC Object Counting πŸ’ŠπŸ’Š

πŸ‘‰PseCo: combining SAM to segment all possible objects as mask proposals & CLIP to classify proposals to obtain accurate object counts. The new SOTA in both few-shot/zero-shot object counting/detection.

πŸ‘‰Review https://t.ly/e4iza
πŸ‘‰Paper https://lnkd.in/dbzMXKWG
πŸ‘‰Repo https://lnkd.in/db9Q9Pse
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πŸ’₯ Announcing #Py4Ai ConferenceπŸ’₯

πŸ‘‰ Super proud to unveil #Py4AI, the newest conference dedicated to exploring the depths of Python & AI. Py4AI is a 1-day free event for Python and Artificial Intelligence developers.

π“π‘πž 𝐟𝐒𝐫𝐬𝐭 π›πšπ­πœπ‘ 𝐨𝐟 𝐬𝐩𝐞𝐚𝐀𝐞𝐫𝐬:
πŸš€Merve Noyan | #HuggingFace πŸ€—
πŸš€Gabriele Lombardi | ARGO Vision
πŸš€Amanda Cercas Curry | Uni. Bocconi
πŸš€Piero Savastano | Cheshire Cat AI
πŸš€Francesco Zuppichini | Zurich Insurance
πŸš€Andrea Palladino, PhD | Sr. Data Scientist

πŸ‘‰ More: https://www.linkedin.com/posts/visionarynet_py4ai-py4ai-python-activity-7152928716988243968-pOUn?utm_source=share&utm_medium=member_desktop
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πŸ’ƒTimeline Text-Driven HumansπŸ’ƒ

πŸ‘‰Novel challenge: timeline control for text-driven motion synthesis of 3D Humans.

πŸ‘‰Review https://t.ly/HLm-N
πŸ‘‰Paper https://lnkd.in/esaR_M_9
πŸ‘‰Project https://lnkd.in/epCZDvFW
πŸ‘‰Repo coming
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πŸ«’ AlphaGeometry: Olympiad-level AI πŸ«’

πŸ‘‰ Theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by
synthesizing millions of theorems and proofs across different levels of complexity 🀯

πŸ‘‰Review https://t.ly/2-Z7C
πŸ‘‰Paper https://lnkd.in/g3QkqwCE
πŸ‘‰Blog https://lnkd.in/ge-mpM7q
πŸ‘‰Repo https://lnkd.in/gHjwks_9
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🦠 XINC: Pixels to Neurons 🦠

πŸ‘‰eXplaining the Implicit Neural Canvas (XINC) from the University of Maryland, is a unified framework for explaining properties of INRs by examining the strength of each neuron’s contribution to each output pixel

πŸ‘‰Review https://t.ly/wwAmz
πŸ‘‰Paper arxiv.org/pdf/2401.10217.pdf
πŸ‘‰Project namithap10.github.io/xinc
πŸ‘‰Repo github.com/namithap10/xinc
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πŸ‘½ One Model <-> All Segmentations πŸ‘½

πŸ‘‰ 10+ different segmentation tasks in one framework, including image-level, video-level, interactive segmentation, & open-vocabulary segmentation. All in one!

πŸ‘‰Review https://t.ly/fywVz
πŸ‘‰Paper https://lnkd.in/dw3S4B74
πŸ‘‰Project https://lnkd.in/dzHT9v45
πŸ‘‰Repo https://lnkd.in/d6fDCnSp
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😻 GARField: Group Anything 😻

πŸ‘‰ GARField is a novel approach for decomposing #3D scenes into a hierarchy of semantically meaningful groups from posed image inputs.

πŸ‘‰Review https://t.ly/6Hkeq
πŸ‘‰Paper https://lnkd.in/d28mfRcZ
πŸ‘‰Project https://lnkd.in/dzYdRNKy
πŸ‘‰Repo (coming) https://lnkd.in/d2VeRJCS
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πŸ”₯ Depth Anything: new SOTA πŸ”₯

πŸ‘‰Depth Anything: the new SOTA in monocular depth estimation (MDE), trained with 1.5M labeled images and 62M+ unlabeled images jointly. It's the new SOTA!

πŸ‘‰Review https://t.ly/tCBwO
πŸ‘‰Paper https://lnkd.in/djx-9k2J
πŸ‘‰Project https://lnkd.in/dYetqZFa
πŸ‘‰Repo https://lnkd.in/d87CrUGv
πŸ‘‰DemoπŸ€— https://lnkd.in/dJhvKBep
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🎭 ULTRA-Realistic Avatar 🎭

πŸ‘‰Novel 3D avatar with enhanced fidelity of geometry, and superior quality of physically based rendering (PBR) textures without unwanted lighting.

πŸ‘‰Review https://t.ly/B3BEu
πŸ‘‰Project https://lnkd.in/dkUQHFEV
πŸ‘‰Paper https://lnkd.in/dtEQxrBu
πŸ‘‰Code coming 🩷
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πŸ”₯Lumiere: SOTA video-genπŸ”₯

πŸ‘‰#Google unveils Lumiere: Space-Time Diffusion Model for Realistic Video Generation. It's the new SOTA, tasks: Text-to-Video, Video Stylization, Cinemagraphs & Video Inpainting.

πŸ‘‰Review https://t.ly/nalJR
πŸ‘‰Paper https://lnkd.in/d-PvrGjT
πŸ‘‰Project https://t.ly/gK8hz
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πŸ§ͺ SUPIR: SOTA restoration πŸ§ͺ

πŸ‘‰SUPIR is the new SOTA in image restoration; suitable for restoration of blurry objects, defining the material texture of objects, and adjusting restoration based on high-level semantics

πŸ‘‰Review https://t.ly/wgObH
πŸ‘‰Project https://supir.xpixel.group/
πŸ‘‰Paper https://lnkd.in/dZPYcUuq
πŸ‘‰Demo coming 🩷 but no code announced :(
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🫧 SAM + Open Models 🫧

πŸ‘‰Grounded SAM (w/ DINO) as an open-set detector to combine with SAM. It can seamlessly integrate with other Open-World models to accomplish more intricate visual tasks.

πŸ‘‰Review https://t.ly/FwasQ
πŸ‘‰Paper arxiv.org/pdf/2401.14159.pdf
πŸ‘‰Code github.com/IDEA-Research/Grounded-Segment-Anything
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πŸ‘’"Virtual Try-All" by #Amazon πŸ‘’

πŸ‘‰#Amazon announces ”Diffuse to Choose”: diffusion-based image-conditioned inpainting for VTON. Virtually place any e-commerce item in any setting.

πŸ‘‰Review https://t.ly/at07Y
πŸ‘‰Paper https://lnkd.in/dxR7nGtd
πŸ‘‰Project diffuse2choose.github.io/
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🦩 WildRGB-D: Objects in the Wild 🦩

πŸ‘‰#NVIDIA unveils a novel RGB-D object dataset captured in the wild: ~8500 recorded objects, ~20,000 RGBD videos, 46 categories with corresponding masks and 3D point clouds.

πŸ‘‰Review https://t.ly/WCqVz
πŸ‘‰Data github.com/wildrgbd/wildrgbd
πŸ‘‰Paper arxiv.org/pdf/2401.12592.pdf
πŸ‘‰Project wildrgbd.github.io/
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πŸŒ‹EasyVolcap: Accelerating Neural VolumetricπŸŒ‹

πŸ‘‰Novel #PyTorch library for accelerating neural video:volumetric video capturing, reconstruction & rendering

πŸ‘‰Review https://t.ly/8BISl
πŸ‘‰Paper arxiv.org/pdf/2312.06575.pdf
πŸ‘‰Code github.com/zju3dv/EasyVolcap
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πŸ™ Rock-Track announced! πŸ™

πŸ‘‰Rock-Track: the evolution of Poly-MOT, the previous SOTA in 3D MOT Tracking-By-Detection framework.

πŸ‘‰Review https://t.ly/hC0ak
πŸ‘‰Repo, coming: https://lnkd.in/dtDkPwCC
πŸ‘‰Paper coming
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🧠350+ Free #AI Courses by #Google🧠

πŸ‘‰350+ free courses from #Google to become professional in #AI & #Cloud. The full catalog (900+) includes a variety of activity: videos, documents, labs, coding, and quizzes. 15+ supported languages. No excuse.

βœ…π†πžπ§πžπ«πšπ­π’π―πž π€πˆ
βœ…πˆπ§π­π«π¨ 𝐭𝐨 π‹π‹πŒπ¬
βœ…π‚π• 𝐰𝐒𝐭𝐑 𝐓𝐅
βœ…πƒπšπ­πš, πŒπ‹, π€πˆ
βœ…π‘πžπ¬π©π¨π§π¬π’π›π₯𝐞 π€πˆ

πŸ‘‰Review: https://t.ly/517Dr
πŸ‘‰Full list: https://www.cloudskillsboost.google/catalog?page=1
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