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
显示更多📈 Telegram 频道 AI with Papers - Artificial Intelligence & Deep Learning 的分析概览
频道 AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 17 055 名订阅者，在 技术与应用 类别中位列第 7 629，并在 马来西亚 地区排名第 2 198 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 17 055 名订阅者。
根据 14 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -138，过去 24 小时变化为 -1，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 18.73%。内容发布后 24 小时内通常能获得 7.49% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 3 195 次浏览，首日通常累积 1 278 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 16。
- 主题关注点： 内容集中在 framework, object, dataset, tba, depth 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“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”
凭借高频更新（最新数据采集于 15 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
17 055
订阅者
-124 小时
-177 天
-13830 天
数据加载中...
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 14 七月 | +1 | |||
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频道帖子
🦧 MonkeyOCRv2 is out! 🦧
👉MonkeyOCRv2 is a text-centric visual foundation model that unifies fine-grained text modeling, cross-task representation learning, and cross-lingual generalization in a single encoder. Released for academic research and non-commercial use💙
👉Review https://t.ly/yicEK
👉Paper https://arxiv.org/pdf/2607.11562
👉Repo https://github.com/Yuliang-Liu/MonkeyOCRv2
| 2 |  🎂REMIND: long-term MOT re-ID🎂
👉REMIND by CVAR-UPM is a novel online tracker designed for long-term multi-object re-ID of generic indoor objects from monocular RGB, requiring neither camera pose nor depth. Repo under MIT💙
👉Review https://t.ly/AkQoI
👉Paper https://lnkd.in/dm58mkCv
👉Project https://lnkd.in/dZrAZqFe
👉Repo https://lnkd.in/dbidrwxU | 1 336 |
| 3 |  🌔Foundation Global SFM🌔
👉Glob3R is a global SfM-style reconstruction built on 3D foundation models. key idea: explicitly optimize feed-forward geometric predictions. Repo TBA💙
👉Review https://t.ly/Z_4C7
👉Paper https://arxiv.org/pdf/2607.09225
👉Project https://junyuandeng.github.io/Glob3r/
👉Repo TBA | 1 644 |
| 4 |  💋SAM-MT: Real-Time Multi-Target VOS💋
👉Fudan & Shangai unveil SAM-MT, an efficient interactive multi-target video segmentation framework that maintains near-single-object efficiency (FPS/VRAM) as target count increases, while maintaining robust video segmentation performance. Repo available💙
👉Review https://t.ly/Z_4C7
👉Paper https://lnkd.in/dvS-iyBD
👉Project https://lnkd.in/daQ8na8T
👉Repo https://lnkd.in/dgbX2tZv | 2 197 |
| 5 |  🔥ZipDepth: Depth on Any Device🔥
👉ZipDepth from UniBO is a compact monocular depth network that bridges this gap by combining an efficient reparameterizable encoder-decoder with large-scale knowledge distillation from a foundation model. Repo under MIT💙
👉Review https://t.ly/qYrLZ
👉Paper https://arxiv.org/pdf/2607.08771
👉Project https://zipdepth.github.io/
👉Repo https://github.com/fabiotosi92/ZipDepth | 2 438 |
| 6 |  🏵️SoccerNet 2026 Results🏵️
👉The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video understanding💙
👉Review https://t.ly/sfD4T
👉Paper https://lnkd.in/dSBgW_3s
👉Project https://lnkd.in/dfdmuvG8 | 2 315 |
| 7 |  🐈⬛Spatial-perception native ViT🐈⬛
👉LingBot-Vision, a vision foundation model pretrained to be spatial-perception native. Better than 7x bigger foundational models. Repo under Apache💙
👉Review https://t.ly/9xIso
👉Paper https://arxiv.org/pdf/2607.05247
👉Project https://technology.robbyant.com/lingbot-vision
👉Repo https://github.com/robbyant/lingbot-vision | 2 874 |
| 8 |  🏯Worldwide Semantic Facade🏯
👉A centimeter-accurate / cross-continental facade point clouds, with fine-grained semantic segmentation of architectural elements, and hierarchical facade taxonomy. 2.7B Dataset💙
👉Review https://t.ly/PpyFD
👉Paper https://arxiv.org/pdf/2607.02018
👉Project jiangyuanwangyi.github.io/UnderOneFacade_official
👉Data drive.google.com/drive/folders/1Yzz7PmyeK1qeOtkTFCfkbw7IEHXcMJo8 | 3 002 |
| 9 |  🔥Nvidia SpatialClaw is out🔥
👉From Nvidia a novel training-free framework for spatial reasoning that adopts code as the action interface. SpatialClaw lets a VLM-backed agent write Python in a persistent kernel, composing perception modules, inspecting intermediate results, and revising its strategy across steps. Impressive: +11.2 points on 20 benchmarks💙
👉Review https://t.ly/7JB0x
👉Paper https://arxiv.org/pdf/2606.13673
👉Project https://spatialclaw.github.io/
👉Repo https://github.com/NVlabs/SpatialClaw | 3 995 |
| 10 |  🌒LUNA: Universal 3D Human Animation🌔
👉LUNA by HKUST + META is a novel LBS-free universal neural animation model that directly maps multiple 2D controls like images, keypoints, sketch and unseen characters into 3D-G deformations, bypassing explicit body fitting.
👉Review https://t.ly/ZX9Ex
👉Paper https://arxiv.org/pdf/2606.31981
👉Project https://penghtyx.github.io/LUNA/
👉Repo N/A 🥲 | 3 287 |
| 11 |  🛸PriorEye: Geospatial Self-Driving🛸
👉MRG (Oxford) introduces geospatial visual priors to leverage the street-level images in autonomous driving. Consistent improvement in performance. Repo under Apache💙
👉Review https://t.ly/7Jgav
👉Paper https://lnkd.in/dYeD2m7n
👉Project https://lnkd.in/dWJvNemr
👉Repo https://lnkd.in/dNExGGtx | 2 833 |
| 12 |  🍀OctoSense: Open Sensing🍀
👉OctoSense is an open-source sensor platform with stereo RGB and event cameras, LiDAR, a thermal camera, an inertial measurement unit, RTK-corrected global positioning system, and proprioception.
👉Review https://t.ly/oFN8L
👉Paper https://lnkd.in/dM3zpyju
👉Project https://lnkd.in/ddrQ3uJ6
👉Repo https://lnkd.in/dhSDjSfG | 2 819 |
| 13 |  👋 Hi everyone!
Over the past few weeks, the number of join requests has increased dramatically, which unfortunately also means a much higher number of spam and bots (in the last days around five hundreds been cut off)
To help me distinguish real people from fake profiles - and avoid rejecting genuine requests by mistake - I'd really appreciate if your profile includes:
📷 A real profile photo
👤 Your full name (or something reasonably identifiable)
💬 If you contact me, please use English if possible.
I don't speak Russian, Arabic, or Chinese, so if your profile and messages are only in those languages, it's very difficult for me to tell whether you're a real person or an automated account. Thank you for your understanding and for helping keep this damn community welcoming and spam-free!
With love,
Alessandro 😈 | 3 254 |
| 14 |  🔊VolHuMe - Volumetric Human Meshes🔊
👉VolHuMe (H/T @Martinella_94) is a novel, high-resolution large-scale dataset of volumetric human meshes with complete 4D GT: multi-view RGB-D, textured meshes, dense point clouds, normal maps, rigged assets, garment segmentation, and SMPL-X fittings in one dataset. Insane💙
👉Review https://t.ly/b5vxy
👉Paper https://arxiv.org/pdf/2606.23062
👉Project giuli13.github.io/volhume-website/#
👉Repo TBA soon | 3 177 |
| 15 |  🕷️Human Universal Grasping🕷️
👉HUG is a flow-matching model that generates diverse human grasps for any user-specified object in a single RGB-D image captured from a stereo camera.
👉Review https://t.ly/VG1Eu
👉Paper https://arxiv.org/pdf/2606.17054
👉Repo https://github.com/KevinyWu/hug
👉Project https://grasping.io/ | 4 698 |
| 16 |  🔍 Nvidia Locate Anything 🔍
👉Diverse localization tasks under a unified vision-language model, including document understanding, GUI grounding, dense detection, and OCR. Repo released💙
👉Review https://t.ly/PvwFo
👉Paper https://lnkd.in/dWfNpzPZ
👉Project https://lnkd.in/dM89BX-8
👉Repo https://lnkd.in/dC4KCQSM | 5 789 |
| 17 |  🪔Latent Decoding with Pixel Diffusion🪔
👉PiD by Nvidia is a plug-and-play diffusion decoder that replaces VAE/RAE decoders, turning latent representations directly into super-resolved pixels in a single pass. Repo under Apache 2.0💙
👉Review https://t.ly/y19mA
👉Paper https://lnkd.in/duVC25C2
👉Project https://lnkd.in/dW6TkzCB
👉Repo https://lnkd.in/dnGdgKRr | 5 421 |
| 18 |  🍒Count Anything, Any Granularity🍒
👉Open-world counting as multi-grained counting, where visual exemplars specify target appearance and fine-grained text specifies the intended semantic granularity across five explicit levels. Repo/Data under Apache💙
👉Review https://t.ly/nqz80
👉Paper https://lnkd.in/dp7khTRU
👉Project https://lnkd.in/d_jfX_Yn
👉Repo https://lnkd.in/dkTRGZkG
👉Data https://lnkd.in/dB83jRyT | 5 537 |
| 19 |  🦄Unified Correspondence Transformer🦄
👉UniCorrn is the first correspondence model with shared weights that unifies 2D-2D, 2D-3D, and 3D-3D geometric matching with an end-to-end transformer architecture. Repo under CC BY-NC-SA 4.0💙
👉Review https://t.ly/2OBdq
👉Paper https://arxiv.org/pdf/2605.04044
👉Project https://neu-vi.github.io/UniCorrn/
👉Repo https://github.com/neu-vi/UniCorrn | 5 415 |
| 20 | About the frequency of posting in the channel: | 4 497 |
