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📈 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 17 262 名订阅者，在 技术与应用 类别中位列第 7 491，并在 马来西亚 地区排名第 2 128 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 17 262 名订阅者。
根据 23 十月, 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 255，过去 24 小时变化为 2，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 18.93%。内容发布后 24 小时内通常能获得 7.17% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 3 267 次浏览，首日通常累积 1 237 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 20。
- 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 24 十月, 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。
17 262
订阅者
+224 小时
+67 天
+25530 天
帖子存档
💥Geo4D: VideoGen 4D Scene💥
👉The Oxford VGG unveils Geo4D: video diffusion for monocular 4D reconstruction. Only synthetic data for training, but strong generalization to real world: point maps, depth & ray maps for the new SOTA in dynamic reconstruction. Code released💙
👉Review https://t.ly/X55Uj
👉Paper arxiv.org/pdf/2504.07961
👉Project geo4d.github.io/
👉Code github.com/jzr99/Geo4D
🥊 Pose in Combat Sports 🥊
👉The novel SOTA framework for an accurate physics-based #3D human pose estimation in combat sports w/ sparse multi-cameras setup. Dataset to be released soon💙
👉Review https://t.ly/EfcGL
👉Paper https://lnkd.in/deMMrKcA
👉Project https://lnkd.in/dkMS_UrH
🧊BoxDreamer Object Pose🧊
👉BoxDreamer is a generalizable RGB-based approach for #3D object pose estimation in the wild, specifically designed to address challenges in sparse-view settings. Code coming, demo released💙
👉Review https://t.ly/e-vX9
👉Paper arxiv.org/pdf/2504.07955
👉Project https://lnkd.in/djz8jqn9
👉Repo https://lnkd.in/dfuEawSA
🤗Demo https://lnkd.in/dVYaWGcS
💛 Unified Scalable SVG Generator 💛
👉OmniSVG is the first family of e2e multimodal generators that leverages pre-trained VLMs to create detailed SVGs. Code, models & dataset to be released under MIT💙
👉Review https://t.ly/JcR3I
👉Paper https://arxiv.org/pdf/2504.06263
👉Project https://omnisvg.github.io/
👉Repo github.com/OmniSVG/OmniSVG
👉Dataset https://huggingface.co/OmniSVG
🐈 TTT Long Video Generation🐈
👉A novel architecture for video generation adapting the CogVideoX 5B model by incorporating Test-Time Training layers. Adding TTT layers into a pre-trained Transformer -> one-minute clip from text storyboards. Videos, code & annotations released💙
👉Review https://t.ly/mhlTN
👉Paper arxiv.org/pdf/2504.05298
👉Project test-time-training.github.io/video-dit/
👉Repo github.com/test-time-training/ttt-video-dit
⛽ VoRA: Vision as LoRA ⛽
👉#ByteDance unveils Vision as LoRA (VoRA), a novel paradigm converting LLMs into Multimodal Large Language Models (MLLMs) by integrating vision-specific LoRA layers. All training data, codes, and model weights available💙
👉Review https://t.ly/guNVN
👉Paper arxiv.org/pdf/2503.20680
👉Repo github.com/Hon-Wong/VoRA
👉Project georgeluimmortal.github.io/vora-homepage.github.io/
🌳 Compose Anything is out 🌳
👉Skywork AI unveils SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e.g., characters, objects, backgrounds) into synthesized videos based on textual prompts. Code, models, & evaluation benchmark released💙
👉Review https://t.ly/MEjzL
👉Paper https://arxiv.org/pdf/2504.02436
👉Project skyworkai.github.io/skyreels-a2.github.io/
👉Repo github.com/SkyworkAI/SkyReels-A2
🤗Models https://huggingface.co/Skywork/SkyReels-A2
💃 Video Motion Graphs 💃
👉#Adobe unveils a novel system designed to generate realistic human motion videos. Using a reference video and conditional signals such as music or motion tags, the system synthesizes amazing new videos. Code & Models to be released💙
👉Review https://t.ly/r4EGF
👉Paper https://lnkd.in/dK_tHyzh
👉Project https://lnkd.in/dE6c_KYZ
👉Repo TBA
🐟Segment Any Motion in Video🐟
👉From CVPR2025 a novel approach for moving object segmentation that combines DINO-based semantic features and SAM2. Code under MIT license💙
👉Review https://t.ly/4aYjJ
👉Paper arxiv.org/pdf/2503.22268
👉Project motion-seg.github.io/
👉Repo github.com/nnanhuang/SegAnyMo
🌳MSVA Zero-Shot Multi-View🌳
👉Niantic unveils MVSA, novel Multi-View Stereo Architecture to work anywhere by generalizing across diverse domains & depth ranges. Highly accurate & 3D-consistent depths. Code & models announced💙
👉Review https://t.ly/LvuTh
👉Paper https://arxiv.org/pdf/2503.22430
👉Project https://nianticlabs.github.io/mvsanywhere/
👉Repo https://lnkd.in/ddQz9eps
🏓LATTE-MV: #3D Table Tennis🏓
👉UC Berkeley unveils at #CVPR2025 a novel system for reconstructing monocular video of table tennis in 3D with uncertainty-aware controller that anticipates opponent actions. Code & Dataset announced, to be released💙
👉Review https://t.ly/qPMOU
👉Paper arxiv.org/pdf/2503.20936
👉Project sastry-group.github.io/LATTE-MV/
👉Repo github.com/sastry-group/LATTE-MV
🦎 Scaling Vision to 4K🦎
👉PS3 by #Nvidia (+UC Berkeley) to scale-up CLIP-style vision pre-training to 4K with *near-constant* cost. Encoding LR global image and selectively processes only informative HR regions. Impressive work. Code/weights & 🤗 announced💙
👉Review https://t.ly/WN479
👉Paper https://lnkd.in/ddWq8UpX
👉Project https://lnkd.in/dMkTY8-k
👉Repo https://lnkd.in/d9YSB6yv
🔥 Dereflection Any Image 🔥
👉SJTU & #Huawei unveils DAI, novel diffusion-based framework able to recover from a wide range of reflection types. One-step diffusion with deterministic outputs & fast inference. Inference, pretrained models & training released💙
👉Review https://t.ly/PDA9K
👉Paper https://arxiv.org/pdf/2503.17347
👉Project abuuu122.github.io/DAI.github.io/
👉Repo github.com/Abuuu122/Dereflection-Any-Image
🙀3D MultiModal Memory🙀
👉M3 is a novel framework by UCSD & #NVIDIA for rendering 3D scenes w/ RGB & foundation model embeddings. Rich spatial & semantic understanding via novel memory system designed to retain multimodal info through videos
👉Review https://t.ly/OrXZO
👉Paper arxiv.org/pdf/2503.16413
👉Project https://lnkd.in/dXAZ97KH
👉Repo https://lnkd.in/dWvunCET
🥎LLM Spatial Understanding🥎
👉SpatialLM by Manycore: novel LLM designed to process 3D point cloud data and generate structured 3D scene understanding outputs. Code, model & data 💙
👉Review https://t.ly/ejr1s
👉Project manycore-research.github.io/SpatialLM/
👉Code github.com/manycore-research/SpatialLM
🤗Models https://huggingface.co/manycore-research
teaser (1) (online-video-cutter.com) (1).mp42.02 MB
🧞 IMPOSSIBLE Videos 🧞
👉IPV-Bench: counterfactual and anti-reality scenes impossible in real world. A novel challenge designed to evaluate and foster progress in video understanding and generation. Code & 🤗-Data 💙
👉Review https://t.ly/D7jhm
👉Paper arxiv.org/pdf/2503.14378
👉Project showlab.github.io/Impossible-Videos/
👉Repo github.com/showlab/Impossible-Videos
🌱 #Py4AI: line-up is official 🌱
👉Last week we announced the first part of our incredible line-up for PY4AI 2025. It's time to disclose the second one and drive you crazy👇
𝐓𝐡𝐞 𝐬𝐞𝐜𝐨𝐧𝐝 𝐛𝐚𝐭𝐜𝐡 𝐨𝐟 𝐬𝐩𝐞𝐚𝐤𝐞𝐫𝐬:
🔥Alfredo Canziani | New York University
🔥Fanny Bouton | OVHcloud
🔥Full list: https://t.ly/JJP8B
🧸 Occluded 3D Reconstruction 🧸
👉Oxford unveils a novel 3D generative model to reconstruct 3D objects from partial observations. Code (TBR), demo, model on HF💙
👉Review https://t.ly/Lr5D7
👉Paper arxiv.org/pdf/2503.13439
👉Project sm0kywu.github.io/Amodal3R/
🤗huggingface.co/spaces/Sm0kyWu/Amodal3R
