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Artificial Intelligence || DL

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Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * LLMs and NLP Admin: @idrokdev

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  • 19 мая🍒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/dB83jRyT1,05%
  • 27 маябез подписи0,91%
  • 28 июл.🍿 Dawn of Generative Cinematography 🍿 🟩 #TheOdyssey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost romantic: massive cameras, film stock, premium lenses, and an obsessive pursuit of the highest possible quality at the moment of capture. 👉 Meanwhile, #AI research is heading in the exact opposite direction. 🟩 A pre-print paper released today, "Camera Anything", demonstrates something that sounded like science fiction just a few years ago: you film a scene once... and then you can virtually reposition the camera anywhere. 👉More https://t.ly/Kd7RV 👉Paper arxiv.org/pdf/2607.24591 👉Project yixuanli98.github.io/cameraanything/ 👉Repo github.com/yixuanli98/CameraAnything0,65%
  • 23 июн.🕷️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/0,48%
  • 3 авг.🐠Dual-branch ID-Tracking🐠 👉TIDE: tracking dense, homogeneous targets, providing a scalable dual-branch design to accommodate diverse hardware constraints. MIT license💙 👉Review https://t.ly/WEDeY 👉Paper https://arxiv.org/pdf/2607.26412 👉Project https://vranlee.github.io/TIDE/ 👉Repo https://github.com/vranlee/TIDE0,00%
  • 24 июл.💢Unified Video Dense Prediction💢 👉UniD predicts: depth, surface normals, semantic segmentation, boundaries, human parts, albedo, shading, and materials. Code TBR💙 👉Review https://t.ly/oo7et 👉Paper https://arxiv.org/pdf/2607.21592 👉Project https://unid-video.github.io/ 👉Repo https://github.com/YihongSun/UniD0,00%
  • 22 июл.👉Not a render. Not a concept. This is GENE.01 by Generative Bionics, the Italians coolest scaleup strikes back: in just six months, they turned GENE.01 into a fully functional humanoid platform that can walk, sense and interact. 👉Full-body multimodal skin perceives touch, proximity, force and temperature, bringing Physical AI closer to safe and natural collaboration with people. 👉More: https://t.ly/F3I3A0,00%
  • 17 июл.🏯SOTA Music-to-Dance Gen🏯 👉The Tongyi Lab unveils Wan-Dancer, a novel stable minute-scale synthesis at 720p/30fps across five dance genres. Impressive results, new SOTA on long clip by a large margin. Repo under Apache 2.0💙 👉Review https://t.ly/AKY5j 👉Paper https://lnkd.in/d_xA7dwb 👉Project https://lnkd.in/dzfnw2h4 👉Repo https://lnkd.in/d-Zj_cTf0,00%
  • 15 июл.🌈FlowWAM: flow->action prediction🌈 👉FlowWAM is a novel dual-stream diffusion framework that adopts optical flow as a unified, video-native action representation. Repo under Apache💙 👉Review https://t.ly/FmutT 👉Paper https://arxiv.org/abs/2607.13017 👉Project https://flow-wam.github.io/ 👉Repo github.com/YixiangChen515/FlowWAM0,00%
  • 14 июл.🔥ZipDepth: Depth on Any Device🔥 👉ZipDepth from UniBO is a super-compact monocular depth network 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/ZipDepth0,00%
  • 13 июл.🌔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 TBA0,00%
  • 13 июл.💋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/dgbX2tZv0,00%