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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๐Ÿœ REM: Segment What You Describe ๐Ÿœ

๐Ÿ‘‰REM is a framework for segmenting concepts in video that can be described via LLM. Suitable for rare & non-object dynamic concepts, such as waves, smoke, etc. Code & Data announced ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/OyVtV
๐Ÿ‘‰Paper arxiv.org/pdf/2410.23287
๐Ÿ‘‰Project https://miccooper9.github.io/projects/ReferEverything/
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โ˜€๏ธ Universal Relightable Avatars โ˜€๏ธ

๐Ÿ‘‰#Meta unveils URAvatar, photorealistic & relightable avatars from phone scan with unknown illumination. Stunning results!

๐Ÿ‘‰Review https://t.ly/U-ESX
๐Ÿ‘‰Paper arxiv.org/pdf/2410.24223
๐Ÿ‘‰Project junxuan-li.github.io/urgca-website
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๐Ÿฃ CityGaussianV2: Large-Scale City ๐Ÿฃ

๐Ÿ‘‰A novel approach for large-scale scene reconstruction that addresses critical challenges related to geometric accuracy and efficiency: 10x compression, 25% faster & -50% memory! Source code released๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/Xgn59
๐Ÿ‘‰Paper arxiv.org/pdf/2411.00771
๐Ÿ‘‰Project dekuliutesla.github.io/CityGaussianV2/
๐Ÿ‘‰Code github.com/DekuLiuTesla/CityGaussian
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๐Ÿ’ช Muscles in Time Dataset ๐Ÿ’ช

๐Ÿ‘‰Muscles in Time (MinT) is a large-scale synthetic muscle activation dataset. MinT contains 9+ hours of simulation data covering 227 subjects and 402 simulated muscle strands. Code & Dataset available soon ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/108g6
๐Ÿ‘‰Paper arxiv.org/pdf/2411.00128
๐Ÿ‘‰Project davidschneider.ai/mint
๐Ÿ‘‰Code github.com/simplexsigil/MusclesInTime
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๐Ÿง  Single Neuron Reconstruction ๐Ÿง 

๐Ÿ‘‰SIAT unveils NeuroFly, a framework for large-scale single neuron reconstruction. Formulating neuron reconstruction task as a 3-stage streamlined workflow: automatic segmentation - connection - manual proofreading. Bridging computer vision and neuroscience ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/Y5Xu0
๐Ÿ‘‰Paper https://arxiv.org/pdf/2411.04715
๐Ÿ‘‰Repo github.com/beanli161514/neurofly
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๐Ÿซ  X-Portrait 2: SOTA(?) Portrait Animation ๐Ÿซ 

๐Ÿ‘‰ByteDance unveils a preview of X-Portrait2, the new SOTA expression encoder model that implicitly encodes every minuscule expressions from the input by training it on large-scale datasets. Impressive results but no paper & code announced.

๐Ÿ‘‰Review https://t.ly/8Owh9 [UPDATE]
๐Ÿ‘‰Paper ?
๐Ÿ‘‰Project byteaigc.github.io/X-Portrait2/
๐Ÿ‘‰Repo ?
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โ„๏ธDonโ€™t Look Twice: ViT by RLTโ„๏ธ

๐Ÿ‘‰CMU unveils RLT: speeding up the video transformers inspired by run-length encoding for data compression. Speed the training up and reducing the token count by up to 80%! Source Code announced ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/ccSwN
๐Ÿ‘‰Paper https://lnkd.in/d6VXur_q
๐Ÿ‘‰Project https://lnkd.in/d4tXwM5T
๐Ÿ‘‰Repo TBA
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๐Ÿ”SeedEdit: foundational T2I๐Ÿ”

๐Ÿ‘‰ByteDance unveils a novel T2I foundational model capable of delivering stable, high-aesthetic image edits which maintain image quality through unlimited rounds of editing instructions. No code announced but a Demo is online๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/hPlnN
๐Ÿ‘‰Paper https://arxiv.org/pdf/2411.06686
๐Ÿ‘‰Project team.doubao.com/en/special/seededit
๐Ÿค—Demo https://huggingface.co/spaces/ByteDance/SeedEdit-APP
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๐Ÿ”ฅ 4 NanoSeconds inference ๐Ÿ”ฅ

๐Ÿ‘‰LogicTreeNet: convolutional differentiable logic gate net. with logic gate tree kernels: Computer Vision into differentiable LGNs. Up to 6100% smaller than SOTA, inference in 4 NANOsecs!

๐Ÿ‘‰Review https://t.ly/GflOW
๐Ÿ‘‰Paper https://lnkd.in/dAZQr3dW
๐Ÿ‘‰Full clip https://lnkd.in/dvDJ3j-u
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๐Ÿ›ฅ๏ธ Global Tracklet Association MOT ๐Ÿ›ฅ๏ธ

๐Ÿ‘‰A novel universal, model-agnostic method designed to refine and enhance tracklet association for single-camera MOT. Suitable for datasets such as SportsMOT, SoccerNet & similar. Source code released๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/gk-yh
๐Ÿ‘‰Paper https://lnkd.in/dvXQVKFw
๐Ÿ‘‰Repo https://lnkd.in/dEJqiyWs
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๐Ÿงถ MagicQuill: super-easy Diffusion Editing ๐Ÿงถ

๐Ÿ‘‰MagicQuill is a novel system designed to support users in smart editing of images. Robust UI/UX (e.g., inserting/erasing objects, colors, etc.) under a multimodal LLM to anticipate user intentions in real time. Code & Demos released ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/hJyLa
๐Ÿ‘‰Paper https://arxiv.org/pdf/2411.09703
๐Ÿ‘‰Project https://magicquill.art/demo/
๐Ÿ‘‰Repo https://github.com/magic-quill/magicquill
๐Ÿ‘‰Demo https://huggingface.co/spaces/AI4Editing/MagicQuill
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๐Ÿงฐ EchoMimicV2: Semi-body Human ๐Ÿงฐ

๐Ÿ‘‰Alipay (ANT Group) unveils EchoMimicV2, the novel SOTA half-body human animation via APD-Harmonization. See clip with audio (ZH/ENG). Code & Demo announced๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/enLxJ
๐Ÿ‘‰Paper arxiv.org/pdf/2411.10061
๐Ÿ‘‰Project antgroup.github.io/ai/echomimic_v2/
๐Ÿ‘‰Repo-v2 github.com/antgroup/echomimic_v2
๐Ÿ‘‰Repo-v1 https://github.com/antgroup/echomimic
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โš”๏ธSAMurai: SAM for Trackingโš”๏ธ

๐Ÿ‘‰UWA unveils SAMURAI, an enhanced adaptation of SAM 2 specifically designed for visual object tracking. New SOTA! Code under Apache 2.0๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/yGU0P
๐Ÿ‘‰Paper https://arxiv.org/pdf/2411.11922
๐Ÿ‘‰Repo https://github.com/yangchris11/samurai
๐Ÿ‘‰Project https://yangchris11.github.io/samurai/
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๐Ÿฆ–Dino-X: Unified Obj-Centric LVM๐Ÿฆ–

๐Ÿ‘‰Unified vision model for Open-World Detection, Segmentation, Phrase Grounding, Visual Counting, Pose, Prompt-Free Detection/Recognition, Dense Caption, & more. Demo & API announced ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/CSQon
๐Ÿ‘‰Paper https://lnkd.in/dc44ZM8v
๐Ÿ‘‰Project https://lnkd.in/dehKJVvC
๐Ÿ‘‰Repo https://lnkd.in/df8Kb6iz
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๐ŸŒŽAll Languages Matter: LMMs vs. 100 Lang.๐ŸŒŽ

๐Ÿ‘‰ALM-Bench aims to assess the next generation of massively multilingual multimodal models in a standardized way, pushing the boundaries of LMMs towards better cultural understanding and inclusivity. Code & Dataset ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/VsoJB
๐Ÿ‘‰Paper https://lnkd.in/ddVVZfi2
๐Ÿ‘‰Project https://lnkd.in/dpssaeRq
๐Ÿ‘‰Code https://lnkd.in/dnbaJJE4
๐Ÿ‘‰Dataset https://lnkd.in/drw-_95v
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๐Ÿฆ™ EdgeCape: SOTA Agnostic Pose ๐Ÿฆ™

๐Ÿ‘‰EdgeCap: new SOTA in Category-Agnostic Pose Estimation (CAPE): finding keypoints across diverse object categories using only one or a few annotated support images. Source code released๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/4TpAs
๐Ÿ‘‰Paper https://arxiv.org/pdf/2411.16665
๐Ÿ‘‰Project https://orhir.github.io/edge_cape/
๐Ÿ‘‰Code https://github.com/orhir/EdgeCape
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๐Ÿ›Ÿ StableAnimator: ID-aware Humans ๐Ÿ›Ÿ

๐Ÿ‘‰StableAnimator: first e2e ID-preserving diffusion for HQ videos without any post-processing. Input: single image + sequence of poses. Insane results!

๐Ÿ‘‰Review https://t.ly/JDtL3
๐Ÿ‘‰Paper https://arxiv.org/pdf/2411.17697
๐Ÿ‘‰Project francis-rings.github.io/StableAnimator/
๐Ÿ‘‰Code github.com/Francis-Rings/StableAnimator
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๐ŸงถSOTA track-by-propagation๐Ÿงถ

๐Ÿ‘‰SambaMOTR is a novel e2e model (based on Samba) for long-range dependencies and interactions between tracklets to handle complex motion patterns / occlusions. Code in Jan. 25 ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/QSQ8L
๐Ÿ‘‰Paper arxiv.org/pdf/2410.01806
๐Ÿ‘‰Project sambamotr.github.io/
๐Ÿ‘‰Repo https://lnkd.in/dRDX6nk2
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๐Ÿ‘บHiFiVFS: Extreme Face Swapping๐Ÿ‘บ

๐Ÿ‘‰HiFiVFS: HQ face swapping videos even in extremely challenging scenarios (occlusion, makeup, lights, extreme poses, etc.). Impressive results, no code announced๐Ÿ˜ข

๐Ÿ‘‰Review https://t.ly/ea8dU
๐Ÿ‘‰Paper https://arxiv.org/pdf/2411.18293
๐Ÿ‘‰Project https://cxcx1996.github.io/HiFiVFS
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๐Ÿ”ฅVideo Depth without Video Models๐Ÿ”ฅ

๐Ÿ‘‰RollingDepth: turning a single-image latent diffusion model (LDM) into the novel SOTA depth estimator. It works better than dedicated model for depth ๐Ÿคฏ Code under Apache๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/R4LqS
๐Ÿ‘‰Paper https://arxiv.org/pdf/2411.19189
๐Ÿ‘‰Project https://rollingdepth.github.io/
๐Ÿ‘‰Repo https://github.com/prs-eth/rollingdepth
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