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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
Ko'proq ko'rsatish📈 Telegram kanali AI with Papers - Artificial Intelligence & Deep Learning analitikasi
AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 17 154 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 726-o'rinni va Malayziya mintaqasida 2 240-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 154 obunachiga ega bo‘ldi.
21 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -166 ga, so‘nggi 24 soatda esa -6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 23.63% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 6.86% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 4 057 marta ko‘riladi; birinchi sutkada odatda 1 177 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 26 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent framework, object, dataset, tba, depth kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 22 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
17 154
Obunachilar
-624 soatlar
-277 kunlar
-16630 kunlar
Postlar arxiv
🔥🔥🔥🔥🔥 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
🏆Who's the REAL SOTA tracker in the world?🏆
👉BofN meta-tracker outperforms, by a large margin, existing SOTA trackers on nine standard benchmarks (LaSOT, TrackingNet, GOT-10K, VOT2019, VOT2021, VOT2022, UAV123, OTB100, and WebUAV-3M). Source Code available💙
👉Review https://t.ly/WB9AR
👉Paper https://arxiv.org/pdf/2407.15707
👉Code https://github.com/BasitAlawode/Best_of_N_Trackers
🎭 TRG: new SOTA in 6DoF Head 🎭
👉ECE (Korea) unveils TRG, a novel landmark-based method for estimating a 6DoF head pose which stands out for its explicit bidirectional interaction structure. Experiments on ARKitFace & BIWI confirm it's the new SOTA. Source Code & Models to be released💙
👉Review https://t.ly/lOIRA
👉Paper https://lnkd.in/dCWEwNyF
👉Code https://lnkd.in/dzRrwKBD
🧿 Shape of Motion for 4D 🧿
👉 Google (+Berkeley) unveils a novel method capable of reconstructing generic dynamic scenes, featuring explicit, full-sequence-long 3D motion, from casually captured monocular videos. Impressive tracking capabilities. Source Code released 💙
👉Review https://t.ly/d9RsA
👉Project https://shape-of-motion.github.io/
👉Paper arxiv.org/pdf/2407.13764
👉Code github.com/vye16/shape-of-motion/
Hi folks,
I need you help 🙏
👉 Could you help me understanding what do you think about the lasting of the hiring process for #artificialintelligence roles? Any comment here will be appreciated :)
Vote here: https://t.ly/UMRXH
Thanks <3
📈Gradient Boosting Reinforcement Learning📈
👉#Nvidia unveils GBRL, a framework that extends the advantages of Gradient Boosting Trees to the RL domain. GBRL adapts the power of Gradient Boosting Trees to the unique challenges of RL environments, including non-stationarity and absence of predefined targets. Code released💙
👉Review https://t.ly/zv9pl
👉Paper https://arxiv.org/pdf/2407.08250
👉Code https://github.com/NVlabs/gbrl
💌 KineTy: Typography Diffusion 💌
👉GIST introduces a novel realistic kinetic typography generation driven by text description. Guided video diffusion models to achieve visually-pleasing text appearances. Repo to be released under Attribution-NC 4.0💙
👉Review https://t.ly/2FWo9
👉Paper arxiv.org/pdf/2407.10476
👉Project seonmip.github.io/kinety/
👉Repo github.com/SeonmiP/KineTy/tree/main
💌 KineTy: Typography Diffusion 💌
👉GIST introduces a novel realistic kinetic typography generation driven by text description. Guided video diffusion models to achieve visually-pleasing text appearances. Repo to be released under Attribution-NC 4.0💙
👉Review https://t.ly/2FWo9
👉Paper arxiv.org/pdf/2407.10476
👉Project seonmip.github.io/kinety/
👉Repo github.com/SeonmiP/KineTy/tree/main
🥥 OmniNOCS: largest 3D NOCS 🥥
👉OmniNOCS by #Google (+Georgia) is a unified NOCS (Normalized Object Coordinate Space) dataset that contains data across different domains with 90+ object classes. The largest NOCS dataset to date. Data & Code available under Apache 2.0💙
👉Review https://t.ly/xPgBn
👉Paper arxiv.org/pdf/2407.08711
👉Project https://omninocs.github.io/
👉Data github.com/google-deepmind/omninocs
