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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🐕 Gaze-LLE: Neural Gaze 🐕

👉Gaze-LLE: novel transformer framework that streamlines gaze target by leveraging features from frozen DINOv2 encoder. Code & models under MIT 💙

👉Review https://t.ly/SadoF
👉Paper arxiv.org/pdf/2412.09586
👉Repo github.com/fkryan/gazelle
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🫶 Dynamic Cam-4D Hands 🫶

👉The Imperial College unveils Dyn-HaMR, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Code announced under MIT💙

👉Review https://t.ly/h5vV7
👉Paper arxiv.org/pdf/2412.12861
👉Project dyn-hamr.github.io/
👉Repo github.com/ZhengdiYu/Dyn-HaMR
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🍄 Open-MLLMs Self-Driving 🍄

👉OpenEMMA: a novel open-source e2e framework based on MLLMs (via Chain-of-Thought reasoning). Effectiveness, generalizability, and robustness across a variety of challenging driving scenarios. Code released under Apache 2.0💙

👉Review https://t.ly/waLZI
👉Paper https://arxiv.org/pdf/2412.15208
👉Code https://github.com/taco-group/OpenEMMA
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🔄️ Orient Anything in 3D 🔄️

👉Orient Anything is a novel robust image-based object orientation estimation model. By training on 2M rendered labeled images, it achieves strong zero-shot generalization in the wild. Code released💙

👉Review https://t.ly/ro5ep
👉Paper arxiv.org/pdf/2412.18605
👉Project orient-anything.github.io/
👉Code https://lnkd.in/d_3k6Nxz
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TOP 10 Papers you loved - 2024

👉Here the list of my posts you liked the most in 2024, thank you all 💙

𝐏𝐚𝐩𝐞𝐫𝐬:
"Look Ma, no markers"
T-Rex 2 Detector
Models at Any Resolution

👉The full list with links: https://t.ly/GvQVy
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🌳 HD Video Object Insertion 🌳

👉VideoAnydoor is a novel zero-shot video object insertion #AI with high-fidelity detail preservation and precise motion control. All-in-one: video VTON, face swapping, logo insertion, multi-region editing, etc.

👉Review https://t.ly/hyvRq
👉Paper arxiv.org/pdf/2501.01427
👉Project videoanydoor.github.io/
👉Repo TBA
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What is your favorite source for the AI updates?
Final Results
32%
Linkedin
4%
Instagram
3%
Reddit
52%
Telegram
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🥮 SOTA probabilistic tracking🥮

👉ProTracker is a novel framework for robust and accurate long-term dense tracking of arbitrary points in videos. Code released under CC Attribution-NonCommercial💙

👉Review https://t.ly/YY_PH
👉Paper https://arxiv.org/pdf/2501.03220
👉Project michaelszj.github.io/protracker/
👉Code github.com/Michaelszj/pro-tracker
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🧤World-Space Ego 3D Hands🧤

👉The Imperial College unveils HaWoR, a novel world-space 3D hand motion estimation for egocentric videos. The new SOTA on both cam pose estimation & hand motion reconstruction. Code under Attribution-NC-ND 4.0 Int.💙

👉Review https://t.ly/ozJn7
👉Paper arxiv.org/pdf/2501.02973
👉Project hawor-project.github.io/
👉Code github.com/ThunderVVV/HaWoR
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🔥 "Nuclear" AI vs. Hyper-Cheap Inference 🔥

What do you expect in 2025 after the #Nvidia announcements at CES 2025? Free to comment :)
Anonymous Poll
23%
🤲Portabile Training Workstation
35%
⚛️Nuclear energy for AI training
33%
🖲️Cheaper Only-inference devices
9%
💰Cloud-intensive Only-inference
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FIFA 3D Human Pose

👉#FIFA WorldPose is a novel dataset for multi-person global pose estimation in the wild, featuring footage from the 2022 World Cup. 2.5M+ annotation, released 💙

👉Review https://t.ly/kvGVQ
👉Paper arxiv.org/pdf/2501.02771
👉Project https://lnkd.in/d5hFWpY2
👉Dataset https://lnkd.in/dAphJ9WA
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🔥 Depth Any Camera (SOTA) 🔥

👉DAC is a novel and powerful zero-shot metric depth estimation framework that extends a perspective-trained model to effectively handle cams with varying FoVs (including large fisheye & 360◦). Code announced (not available yet)💙

👉Review https://t.ly/1qz4F
👉Paper arxiv.org/pdf/2501.02464
👉Project yuliangguo.github.io/depth-any-camera/
👉Repo github.com/yuliangguo/depth_any_camera
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❤️‍🔥 Uncommon object in #3D ❤️‍🔥

👉#META releases uCO3D, a new object-centric dataset for 3D AI. The largest publicly-available collection of HD videos of objects with 3D annotations that ensures full-360◦ coverage. Code & data under CCA 4.0💙

👉Review https://t.ly/Z_tvA
👉Paper https://arxiv.org/pdf/2501.07574
👉Project https://uco3d.github.io/
👉Repo github.com/facebookresearch/uco3d
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🏆Universal Detector-Free Match🏆

👉MatchAnything: novel detector-free universal matcher across unseen real-world single/cross-modality domains. Same weights for everything. Code announced, to be released 💙

👉Review https://t.ly/sx92L
👉Paper https://lnkd.in/dWwRwGyY
👉Project https://lnkd.in/dCwb2Yte
👉Repo https://lnkd.in/dnUXYzQ5
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🆘 Help: Looking for Outstanding Speakers 🆘

👉Who would you suggest as a speaker for your ideal conference on AI (CV, LLM, RAG, ML, HW Optimization, AI & Space, etc.)? Only “hardcore” technical talks, no commercial at all. Please comment here with name, topic and affiliation (es: Paul Gascoigne, Computer Vision & Football, Scotland Team).

Guaranteed tickets & more for the suggestions that will become invited speakers ;)
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🧞‍♂️Omni-RGPT: SOTA MLLM Understanding🧞‍♂️

👉 #NVIDIA presents Omni-RGPT, MLLM for region-level comprehension for both images & videos. New SOTA on image/video-based commonsense reasoning.

👉Review https://t.ly/KHnQ7
👉Paper arxiv.org/pdf/2501.08326
👉Project miranheo.github.io/omni-rgpt/
👉Repo TBA soon
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🔥 GAGA: Group Any Gaussians 🔥

👉GAGA is a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation models. Code available, recently updated💙

👉Review https://t.ly/Nk_jT
👉Paper www.gaga.gallery/static/pdf/Gaga.pdf
👉Project www.gaga.gallery/
👉Repo github.com/weijielyu/Gaga
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🎁Free Book: LLM Foundations🎁

👉A fully free book just released on arXiv to outline the basic concepts of #LLMs and related techniques with a focus on the foundational aspects.

Chapter 1: basics of pre-training
Chapter 2: gen-models & LLMs
Chapter 3: prompting methods
Chapter 4: alignment methods

👉If you have any background in ML, along with a certain understanding of stuff like Transformers, this book will be "smooth". However, even without this prior knowledge, it is still perfectly fine because the contents of each chapter are self-contained.

👉Review https://t.ly/9LGCa
👉Book https://lnkd.in/d3VkswZf
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