اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/ #AI #chatGPT
نمایش بیشتر📈 تحلیل کانال تلگرام AI with Papers - Artificial Intelligence & Deep Learning
کانال AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 17 213 مشترک است و جایگاه 7 718 را در دسته فناوری و برنامهها و رتبه 2 235 را در منطقه ماليزيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 213 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 10 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -123 و در ۲۴ ساعت گذشته برابر 0 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 23.79% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.59% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 4 095 بازدید دریافت میکند. در اولین روز معمولاً 1 307 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 15 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند framework, object, dataset, tba, depth تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision
Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/
#AI #chatGPT”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 11 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
17 213
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آرشیو پست ها
Hinton our guest in Pavia (remotely) 💚😈
🔥BoxerNet: SOTA 2D->3D BBs🔥
👉Boxer by #META: transformer-based network to lift 2D BB proposals into 3D, followed by multi-view fusion and geometric filtering to produce globally consistent de-duplicated 3DBBs in metric world space. Repo under A-NC 4.0 International💙
👉Review https://t.ly/mlmV1
👉Paper https://arxiv.org/pdf/2604.05212
👉Project facebookresearch.github.io/boxer/
👉Repo github.com/facebookresearch/boxer
🔥Vanast: VTON w/ Human Animation🔥
👉SNU unveils a novel unified framework that generates garment-transferred human animation videos directly from a single human/garment images, and pose guidance clip. Repo announced💙
👉Review https://t.ly/c0t79
👉Paper arxiv.org/pdf/2604.04934
👉Project hyunsoocha.github.io/vanast/
👉Repo github.com/snuvclab/vanast
🍎Video Object Deletion🍎
👉Void by Netflix is a novel video object removal framework designed to perform physically-plausible inpainting in very complex scenarios. Repo under Apache 2.0💙
👉Review https://t.ly/cMVny
👉Paper https://arxiv.org/pdf/2604.02296
👉Project https://void-model.github.io/
👉Repo https://github.com/Netflix/void-model
If you have to invest TODAY 1B$ on a frontier tech for the next decade, would you invest in space, agentic, quantum or frugal GPUs? Vote here: https://t.ly/hSx6i
🪬Camera Raw Image Generation🪬
👉RawGen by #Samsung is a generative approach that learns the complex distribution of raw sensor data directly, enabling high-fidelity generation from either text descriptions or standard sRGB images across arbitrary camera sensors. Linear raw image once, then apply any ISP operation. Repo announced💙
👉Review https://t.ly/_QVKP
👉Paper https://arxiv.org/pdf/2604.00093
👉Project https://dy112.github.io/rawgen-page/
👉Repo TBA
🌵SOTA Training-Free In-Context Segmentation🌵
👉INSID3 is the new SOTA, training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. Repo under Apache 2.0💙
👉Review https://t.ly/NVWHN
👉Paper https://arxiv.org/pdf/2603.28480
👉Project https://visinf.github.io/INSID3/
👉Repo https://github.com/visinf/INSID3
👌HandX: Scaling Hands Motion👌
👉 HandX is a unified foundation spanning data, annotation, and evaluation: novel large-scale dataset of bimanual & dexterous motions with fine-grained textual. Around 6M frames. Repo available💙
👉Review https://t.ly/1nGxw
👉Paper https://arxiv.org/pdf/2603.28766
👉Project https://handx-project.github.io/
👉Repo github.com/handx-project/HandX
💥 GaussianGPT 3D GSC💥
👉From TUM, GaussianGPT: transformer-based 3D Gaussians generation via next-token prediction -> full 3D complex indoor scene. Repo announced💙
👉Review https://t.ly/bj-lL
👉Paper https://arxiv.org/pdf/2603.26661
👉Project https://nicolasvonluetzow.github.io/GaussianGPT/
👉Repo TBA
🐍Pose-Appearance-Motion for HOI🐍
👉PAM is a novel Pose–Appearance–Motion Engine for controllable Hand–Object Interaction SOTA video generation. Repo/models available💙
👉Review
👉Paper arxiv.org/pdf/2603.22193
👉Project gasaiyu.github.io/PAM.github.io/
👉Repo https://github.com/GasaiYU/PAM
🦪OccAny: Universal 3D Occupancy🦪
👉OccAny by Valeo is a novel unified framework for generalized unconstrained urban 3D occupancy prediction. Repo under Apache 2.0💙
👉Review
👉Paper https://arxiv.org/pdf/2603.23502
👉Project https://valeoai.github.io/OccAny/
👉Repo https://github.com/valeoai/OccAny
🍓Material-Aware Grouping🍓
👉Material Magic Wand (Adobe) is a tool for material-aware grouping of parts in untextured 3D meshes. Given one selected part, it automatically retrieves the other parts in the same shape by its material. Repo announced💙
👉Review https://t.ly/q00SU
👉Paper https://arxiv.org/pdf/2603.17370
👉Project umangi-jain.github.io/material-magic-wand/
👉Repo TBA
🍧10,000× faster SAM-3D🍧
👉Fast SAM 3D Body achieves up to 10.9× speedup, over 10,000× faster MHR-to-SMPL conversion -> real-time humanoid control from RGB. Repo available💙
👉Review https://t.ly/uHx84
👉Paper https://arxiv.org/pdf/2603.15603
👉Project yangtiming.github.io/Fast-SAM-3D-Body-Page/
👉Repo https://github.com/yangtiming/Fast-SAM-3D-Body
🤖Physically-Plausible Human🤖
👉PhysMoDPO is a novel direct preference optimization framework for humanoid motion generation. Repo under MIT💙
👉Review https://t.ly/clf8w
👉Paper https://arxiv.org/pdf/2603.13228
👉Project https://mael-zys.github.io/PhysMoDPO/
👉Repo https://github.com/Mael-zys/PhysMoDPO
🌈 New SOTA Video Depth 🌈
👉DVD is the new Video Depth Estimation SOTA with full training suite available under Apache2.0💙
👉Review https://t.ly/gpCkG
👉Paper https://arxiv.org/pdf/2603.12250
👉Project https://dvd-project.github.io/
👉Repo github.com/EnVision-Research/DVD
☄️OmniStream: Perceive-Reconstruct-Act ☄️
👉Novel unified streaming visual backbone that effectively perceives, reconstructs, and acts from diverse visual inputs. Repo/Models announced💙
👉Review https://t.ly/_zZMO
👉Paper arxiv.org/pdf/2603.12265
👉Project go2heart.github.io/omnistream/
👉Repo github.com/Go2Heart/OmniStream
🍓Surface Light Tokenizer🍓
👉Apple unveils LITO a novel latent flow matching model enables HQ image-to-3D. Latent representation that encodes a surface light field into a compact set of latent vectors. Impressive results but no code🥲
👉Review https://t.ly/xcWNe
👉Paper https://lnkd.in/dYHwY4YX
👉Project https://lnkd.in/dtJT8bXy
🔥Holistic 3D Spatial Intelligence🔥
👉Holi-Spatial is the first fully automated pipeline capable of converting raw video streams into holistic 3D spatial annotations without human intervention. Code/Data announced💙
👉Review https://t.ly/PDpr9
👉Paper https://lnkd.in/dTbMuZCm
👉Project https://lnkd.in/d66CYB4q
👉Repo https://lnkd.in/dAGzShXj
📊Real-Time Scene Graph📊
👉REACT++ by Umea University is the new state-of-the-art model for real-time SGG: 20% faster with a gain of 10% in relation prediction accuracy on average. Code under MIT💙
👉Review https://t.ly/c12VX
👉Paper https://arxiv.org/pdf/2603.06386
👉Repo https://github.com/Maelic/SGG-Benchmark
🎪SOTA Arbitrary Tracking🎪
👉TAPFormer is the novel SOTA transformer-based framework that performs asynchronous temporal-consistent fusion of frames and events for robust and high-freq point tracking. Repo & Dataset under MIT💙
👉Review https://t.ly/-q4wm
👉Paper https://arxiv.org/pdf/2603.04989
👉Project http://tapformer.github.io/
👉Repo https://github.com/ljx1002/TAPFormer
