AI with Papers - Artificial Intelligence & Deep Learning

前往频道在 Telegram

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

马来西亚2 248 技术与应用7 687...

📈 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 17 236 名订阅者，在 技术与应用 类别中位列第 7 687，并在 马来西亚 地区排名第 2 248 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 17 236 名订阅者。

根据 03 六月, 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 -80，过去 24 小时变化为 -13，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 13.16%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 2 271 次浏览，首日通常累积 0 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 14。
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 04 六月, 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。

17 236

订阅者

-1324 小时

-677 天

-8030 天

2 271

帖子浏览量

无数据24 小时

无数据48 小时

13.16%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

17 236

🔍 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

17 236

🪔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

17 236

🍒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

17 236

🦄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

17 236

About the frequency of posting in the channel:

Anonymous voting

17 236

🪝Syn4D: Multiview Synthetic 4D Dataset🪝 👉Syn4D is novel multi-view synthetic dataset of dynamic scenes that includes ground-truth camera motion, depth maps, dense tracking, and parametric human pose annotations💙 👉Review https://t.ly/SL1mk 👉Paper https://arxiv.org/pdf/2605.05207 👉Project https://jzr99.github.io/Syn4D/ 👉Repo https://github.com/jzr99/Syn4D 👉Data huggingface.co/datasets/Syn4D/Syn4D_RGBD/tree/main

17 236

🧘‍♀️Holistic Shot Boundary Detection🧘‍♀️ 👉OmniShotCut detects shot changes of the video in diverse sources (anime, vlog, game, shorts, sports, screen recording, etc.), and recognize Sudden Jump and Transitions (dissolve, fade, wipe, etc.) by proposing a Shot-Query-based Video Transformer. Repo, demo & benchmark💙 👉Review https://t.ly/sTi7N 👉Paper https://arxiv.org/pdf/2604.24762 👉Project uva-computer-vision-lab.github.io/OmniShotCut_website/ 👉Repo github.com/UVA-Computer-Vision-Lab/OmniShotCut

17 236

🛒 Reshoot-Anything is out 🛒 👉Reshoot-Anything reshoots dynamic monocular videos under novel camera trajectories. Code under Apache 2.0 💙 👉Review https://t.ly/MIqAc 👉Paper https://arxiv.org/pdf/2604.21776 👉Project adithyaiyer1999.github.io/reshoot-anything/ 👉Repo github.com/morphicfilms/video-to-video

17 236

💙 PY4AI 2026: here we are! 💙 👉The third edition of our conference is official! Speaker list and (free) tickets: https://t.ly/L4_52

17 236

🎈Face Anything 4D (SOTA)🎈 👉A novel unified 4D facial reconstruction and dense tracking from image sequences: new SOTA in facial single-image and mono-video depth estimation, dense 4D reconstruction, and 3D point tracking. Repo & Dataset announced💙 👉Review https://t.ly/zItie 👉Paper https://arxiv.org/pdf/2604.19702 👉Project kocasariumut.github.io/FaceAnything 👉Repo TBA

17 236

🌗Mobile Ultra-detailed Avatars🌗 👉Given skeletal poses and a virtual camera as inputs, MUA by Max Planck Institute produces photorealistic renderings and hyper-detailed geometry of animatable clothed humans. Repo announced💙 👉Review https://t.ly/QPCy6 👉Paper https://arxiv.org/pdf/2604.18583 👉Project https://vcai.mpi-inf.mpg.de/projects/MUA/ 👉Repo TBA

17 236

👩‍🦰 3D Head w/ Deformable Hair 👩‍🦰 👉Xi’an Jiaotong University unveils a novel method that reconstructs decoupled 3D Gaussian head avatars from a single input image: effortless hairstyle transfer with natural dynamic hair motion. Code announced💙 👉Review https://t.ly/kWZdd 👉Paper https://arxiv.org/pdf/2604.14782 👉Project yuansun-xjtu.github.io/CompHairHead.io/ 👉Repo yuansun-xjtu.github.io/CompHairHead.io/

17 236

🐞GCT 3D Reconstruction🐞 👉ANT unveils LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. Repo under A-NC 4.0 International💙 👉Review https://t.ly/ExodA 👉Paper https://arxiv.org/pdf/2604.14141 👉Project https://arxiv.org/pdf/2604.14141 👉Repo github.com/robbyant/lingbot-map

17 236

📱3D Human-Object Contact📱 👉Pi-HOC by CMU + NREC is a novel single-pass, instance-aware framework for dense 3D semantic contact prediction of all human-object pairs. Repo announced💙 👉Review https://t.ly/TAgG1 👉Paper https://arxiv.org/pdf/2604.12923 👉Project https://pi-hoc.github.io/ 👉Repo https://github.com/SravanChittupalli/Pi-HOC

17 236

🐓Interactive Objects from EgoVideo🐓 👉EgoFun3D by Simon Fraser University is a coordinated task, dataset and benchmark for modeling interactive 3D objects from egocentric videos. Repo (TBA), demo & dataset💙 👉Review https://t.ly/YhGN7 👉Paper arxiv.org/pdf/2604.11038 👉Project 3dlg-hcvc.github.io/EgoFun3D/ 👉Repo github.com/3dlg-hcvc/EgoFun3D 👉Demo bc79fea884062374b3.gradio.live/

17 236

🧴OmniShow: Automatic Contents Creation🧴 👉OmniShow is the novel SOTA in content creation with industry-grade performance. Impressive results, best with audio. Repo announced💙 👉Review https://t.ly/Pm-7U 👉Paper arxiv.org/pdf/2604.11804 👉Project correr-zhou.github.io/OmniShow/ 👉Repo github.com/Correr-Zhou/OmniShow

17 236

🔥SOTA 3D Detection in the wild🔥 👉WildDet3D is a novel unified geometry-aware architecture that natively accepts text, point, and box prompts and can incorporate auxiliary depth signals at inference time. New SOTA! Repo, models & #iphone💙 👉Review https://t.ly/8NxBN 👉Paper https://arxiv.org/pdf/2604.08626 👉Project https://allenai.github.io/WildDet3D/ 👉Repo https://github.com/allenai/WildDet3D

17 236

🐞6D Object Pose w/ Deformation🐞 👉DeSOPE by Xidian & #MagicLeap is a novel large-scale dataset for 6DoF deformed objects: 665K pose annotations produced via a semiautomatic pipeline. Repo & Dataset announced💙 👉Review https://t.ly/M5VgX 👉Paper https://arxiv.org/pdf/2604.06720 👉Project https://desope-6d.github.io/ 👉Repo TBA

17 236

🪞1.1M Metric VTON Dataset🪞 👉Google's Fit-Inclusive Try-on: large-scale VTO dataset comprising over 1.13M try-on image triplets accompanied by precise body and garment measurements. Repo & dataset announced💙 👉Review https://t.ly/cs-pt 👉Paper arxiv.org/pdf/2604.08526 👉Project johannakarras.github.io/FIT/ 👉Repo TBA

17 236

Here the preview, tomorrow the full clip from official source :)