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
Show more📈 Analytical overview of Telegram channel AI with Papers - Artificial Intelligence & Deep Learning
Channel AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) in the English language segment is an active participant. Currently, the community unites 17 261 subscribers, ranking 7 509 in the Technologies & Applications category and 2 130 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 261 subscribers.
According to the latest data from 25 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 261 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 16.91%. Within the first 24 hours after publication, content typically collects 7.04% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 918 views. Within the first day, a publication typically gains 1 216 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 16.
- Thematic interests: Content is focused on key topics such as framework, object, dataset, tba, depth.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 26 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
17 261
Subscribers
+224 hours
+17 days
+26130 days
Posts Archive
🌏 JoyHallo: Mandarin Digital Human 🌏
👉JD Health faced the challenges of audio-driven video generation in Mandarin, a task complicated by the language’s intricate lip movements and the scarcity of HQ datasets. Impressive results (-> audio ON). Code Models available💙
👉Review https://t.ly/5NGDh
👉Paper arxiv.org/pdf/2409.13268
👉Project jdh-algo.github.io/JoyHallo/
👉Code github.com/jdh-algo/JoyHallo
🌏 JoyHallo: Mandarin Digital Human 🌏
👉JD Health faced the challenges of audio-driven video generation in Mandarin, a task complicated by the language’s intricate lip movements and the scarcity of HQ datasets. Impressive results (-> audio ON). Code Models available💙
👉Review
👉Paper arxiv.org/pdf/2409.13268
👉Project jdh-algo.github.io/JoyHallo/
👉Code github.com/jdh-algo/JoyHallo
⚽ SoccerNet 2024 Results ⚽
👉SoccerNet is the annual video understanding challenge for football. These challenges aim to advance research across multiple themes in football. The 2024 results are out!
👉Review https://t.ly/DUPgx
👉Paper arxiv.org/pdf/2409.10587
👉Repo github.com/SoccerNet
👉Project www.soccer-net.org/
🧸Motion Instruction Fine-Tuning🧸
👉MotIF is a novel method that fine-tunes pre-trained VLMs to equip the capability to distinguish nuanced robotic motions with different shapes and semantic groundings. A work by MIT, Stanford, and CMU. Source Code announced, coming💙
👉Review https://t.ly/iJ2UY
👉Paper https://arxiv.org/pdf/2409.10683
👉Project https://motif-1k.github.io/
👉Code coming
🌭Hand-Object interaction Pretraining🌭
👉Berkeley unveils HOP, a novel approach to learn general robot manipulation priors from 3D hand-object interaction trajectories.
👉Review https://t.ly/FLqvJ
👉Paper https://arxiv.org/pdf/2409.08273
👉Project https://hgaurav2k.github.io/hop/
💄Interactive Drag-based Editing💄
👉CSE unveils InstantDrag: novel pipeline designed to enhance editing interactivity and speed, taking only an image and a drag instruction as input. Source Code announced, coming💙
👉Review https://t.ly/hy6SL
👉Paper arxiv.org/pdf/2409.08857
👉Project joonghyuk.com/instantdrag-web/
👉Code github.com/alex4727/InstantDrag
🫒 Omni Urban Scene Reconstruction 🫒
👉OmniRe is novel holistic approach for efficiently reconstructing HD dynamic urban scenes from on-device logs. It's able to create the simulation of reconstructed scenarios with actors in real-time (~60 Hz). Code released💙
👉Review https://t.ly/SXVPa
👉Paper arxiv.org/pdf/2408.16760
👉Project ziyc.github.io/omnire/
👉Code github.com/ziyc/drivestudio
🐺 Diffusion Game Engine 🐺
👉#Google unveils GameNGen: the first game engine powered entirely by a neural #AI that enables real-time interaction with a complex environment over long trajectories at HQ. No code announced but I love it 💙
👉Review https://t.ly/_WR5z
👉Paper https://lnkd.in/dZqgiqb9
👉Project https://lnkd.in/dJUd2Fr6
🔥🔥🔥🔥🔥 SOURCE CODE IS OUT !!! 🔥🔥🔥🔥🔥
Thanks Danny for the info 🥇
🦧Sapiens: SOTA ViTs for human🦧
👉META unveils Sapiens, a family of models for human-centric vision tasks: 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Source Code announced, coming💙
👉Review https://t.ly/GKQI0
👉Paper arxiv.org/pdf/2408.12569
👉Project rawalkhirodkar.github.io/sapiens
👉Code github.com/facebookresearch/sapiens
🦓 Zebra Detection & Pose 🦓
👉The first synthetic dataset that can be used for both detection and 2D pose estimation of zebras without applying any bridging strategies. Code, results, models, and the synthetic, training/validation data, including 104K manually labeled images open-sourced💙
👉Review https://t.ly/HTEZZ
👉Paper https://lnkd.in/dQYT-fyq
👉Project https://lnkd.in/dAnNXgG3
👉Code https://lnkd.in/dhvU97xD
🏗️ #Adobe Instant TurboEdit 🏗️
👉Adobe unveils a novel real-time text-based disentangled real image editing method built upon 4-step SDXL Turbo. SOTA HQ image editing using ultra fast few-step diffusion. No code announced but easy to guess it will be released in commercial tools.
👉Review https://t.ly/Na7-y
👉Paper https://lnkd.in/dVs9RcCK
👉Project https://lnkd.in/dGCqwh9Z
👉Code 😢
🧪 Click-Attention Segmentation 🧪
👉An interesting image patch-based click attention algorithm and an affinity loss inspired by SASFormer. This novel approach aims to decouple positive and negative clicks, guiding positive ones to focus on the target object and negative ones on the background. Code released under Apache💙
👉Review https://t.ly/tG05L
👉Paper https://arxiv.org/pdf/2408.06021
👉Code https://github.com/hahamyt/ClickAttention
👋 Real-time Expressive Hands 👋
👉Zhejiang unveils XHand, a novel expressive hand avatar designed to comprehensively generate hand shape, appearance, and deformations in real-time. Source Code released (Apache 2.0) the Jul. 31st, 2024💙
👉Review https://t.ly/8obbB
👉Project https://lnkd.in/dRtVGe6i
👉Paper https://lnkd.in/daCx2iB7
👉Code https://lnkd.in/dZ9pgzug
🔥🔥 SAM v2 is out! 🔥🔥
👉#Meta announced SAM 2, the novel unified model for real-time promptable segmentation in images and videos. 6x faster, it's the new SOTA by a large margin. Source Code, Dataset, Models & Demo released under permissive licenses💙
👉Review https://t.ly/oovJZ
👉Paper https://t.ly/sCxMY
👉Demo https://sam2.metademolab.com
👉Project ai.meta.com/blog/segment-anything-2/
👉Models github.com/facebookresearch/segment-anything-2
🪄 Diffusion Models for Transparency 🪄
👉MIT (+ #Google) unveils Alchemist, a novel method to control material attributes of objects like roughness, metallic, albedo & transparency in real images. Amazing work but code not announced🥺
👉Review https://t.ly/U98_G
👉Paper arxiv.org/pdf/2312.02970
👉Project www.prafullsharma.net/alchemist/
🎁 A guide for modern CV 🎁
👉In the last 18 months I received more than 1,100+ applications for research roles. The majority part of the applicants doesn't deeply know a few milestones in CV. Here a short collection of mostly-free resources to spend a bit of good time in the summer.
𝐁𝐨𝐨𝐤𝐬 (recommended):
✅DL with Python https://t.ly/VjaVx
✅Python OOP https://t.ly/pTQRm
𝐎𝐧𝐥𝐢𝐧𝐞 V𝐢𝐝𝐞𝐨 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 (recommended):
✅Berkeley | Modern CV (2023) https://t.ly/AU7S3
𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:
✅PyTorch https://lnkd.in/dTvJbjAx
✅PyTorchLighting https://lnkd.in/dAruPA6T
✅Albumentations https://albumentations.ai/
𝐏𝐚𝐩𝐞𝐫𝐬:
✅EfficientNet https://lnkd.in/dTsT44ae
✅ViT https://lnkd.in/dB5yKdaW
✅UNet https://lnkd.in/dnpKVa6T
✅DeepLabV3+ https://lnkd.in/dVvqkmPk
✅YOLOv1: https://lnkd.in/dQ9rs53B
✅YOLOX: https://lnkd.in/d9ZtsF7g
👉More papers and the full list: https://t.ly/WAwAk
👽Keypoint Promptable Re-ID👽
👉KPR is a novel formulation of the ReID problem that explicitly complements the input BBox with a set of semantic keypoints indicating the intended target. Code, dataset and annotations coming soon💙
👉Review https://t.ly/vCXV_
👉Paper https://arxiv.org/pdf/2407.18112
👉Repo github.com/VlSomers/keypoint_promptable_reidentification
🧱EAFormer: Scene Text-Segm.🧱
👉A novel Edge-Aware Transformers to segment texts more accurately, especially at the edge of texts. FULL re-annotation of COCO_TS and MLT_S! Code coming, data available on 🤗
👉Review https://t.ly/0G2uX
👉Paper https://arxiv.org/pdf/2407.17020
👉Project https://hyangyu.github.io/EAFormer/
👉Data huggingface.co/datasets/HaiyangYu/TextSegmentation/tree/main
🐢 TAPTRv2: new SOTA for TAP 🐢
👉TAPTRv2: Transformer-based approach built upon TAPTR for solving the Tracking Any Point (TAP) task. TAPTR borrows designs from DETR and formulates each tracking point as a point query, making it possible to leverage well-studied operations in DETR-like algorithms. The Source Code of V1 is available, V2 coming💙
👉Review https://t.ly/H84ae
👉Paper v1 https://lnkd.in/d4vD_6xx
👉Paper v2 https://lnkd.in/dE_TUzar
👉Project https://taptr.github.io/
👉Code https://lnkd.in/dgfs9Qdy
