SAM3 Video Segmentation
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Track and label objects in videos using text prompts or clicks
Muhammad Umair
umair894
AI & ML interests
Multimodal Reidentification | Feature Upscaling | Object Tracking |PhD UESTC
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facebook/sam3:Access to this repo has been rejected
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MedSAM3: Delving into Segment Anything with Medical Concepts
Organizations
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#27 opened 26 minutes ago
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umair894
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Introducing Photo-Mate-v2, based on FLUX.1-Kontext-dev, for advanced image manipulation tasks. It supports transforming scenes into top-down/bottom-up perspectives, CAM-right/left-view and its reverse, as well as general kontext-specified object removal. Below is the list of demos and adapters.🔥🤗
➤ Spaces [Demo] : prithivMLmods/Kontext-Photo-Mate-v2
Kontext-Adapters :
✦ Kontext-Bottom-Up-View: prithivMLmods/Kontext-Bottom-Up-View
✦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
✦ Kontext-Top-Down-View: prithivMLmods/Kontext-Top-Down-View
✦ Kontext-CAM-Left-View: prithivMLmods/Kontext-CAM-Left-View
✦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
✦ Kontext-Unblur-Upscale: prithivMLmods/Kontext-Unblur-Upscale
✦ Kontext-0811-exp: prithivMLmods/Kontext-0811-exp
Photo-Mate Collection:
✦ Kontext CAM Angles: https://huggingface.co/collections/prithivMLmods/kontext-cam-angles
✦ i2i - Kontext (exp): https://huggingface.co/collections/prithivMLmods/i2i-kontext-exp
✦ LZO-1 (Lossless Zoom Operator): https://huggingface.co/collections/prithivMLmods/lzo-1-lossless-zoom-operator
Related-Apps:
✦ Photo-Mate [Version 1.0]: prithivMLmods/Photo-Mate-i2i
✦ Image Generation Apps [Collection]: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
To know more about it, visit the app page or the respective model page!
@prithivMLmods
➤ Spaces [Demo] : prithivMLmods/Kontext-Photo-Mate-v2
Kontext-Adapters :
✦ Kontext-Bottom-Up-View: prithivMLmods/Kontext-Bottom-Up-View
✦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
✦ Kontext-Top-Down-View: prithivMLmods/Kontext-Top-Down-View
✦ Kontext-CAM-Left-View: prithivMLmods/Kontext-CAM-Left-View
✦ Kontext-CAM-Right-View: prithivMLmods/Kontext-CAM-Right-View
✦ Kontext-Unblur-Upscale: prithivMLmods/Kontext-Unblur-Upscale
✦ Kontext-0811-exp: prithivMLmods/Kontext-0811-exp
Photo-Mate Collection:
✦ Kontext CAM Angles: https://huggingface.co/collections/prithivMLmods/kontext-cam-angles
✦ i2i - Kontext (exp): https://huggingface.co/collections/prithivMLmods/i2i-kontext-exp
✦ LZO-1 (Lossless Zoom Operator): https://huggingface.co/collections/prithivMLmods/lzo-1-lossless-zoom-operator
Related-Apps:
✦ Photo-Mate [Version 1.0]: prithivMLmods/Photo-Mate-i2i
✦ Image Generation Apps [Collection]: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
To know more about it, visit the app page or the respective model page!
@prithivMLmods
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A small blog post titled - Hall of Multimodal OCR VLMs and Demonstrations has been published on ↗️ https://huggingface.co/blog/prithivMLmods/multimodal-ocr-vlms on behalf of
strangervisionhf
It discusses the latest trends in OCR models, the multilingual support offered by modern OCR systems, their unique capabilities, OCR benchmark model comparisons, transformer-based implementations, and strategies for streamlining
strangervisionhf
It discusses the latest trends in OCR models, the multilingual support offered by modern OCR systems, their unique capabilities, OCR benchmark model comparisons, transformer-based implementations, and strategies for streamlining
transformers compatibility.
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Multilingual Tokenization Showdown
Analyzing 12 LLM Tokenizers Across 204 Languages.
First, I've created a dataset with Wikipedia's "Cat" article text in 272 languages:
Norod78/WikiCat-Multilingual
For each language entry with at least 100 words, I tokenized the text using 12 tokenizers and calculated the "Characters per token" ratio and "Word per token" ratio. The higher this ratio is, the more information each token represents on average for that language (and perhaps allowing the llm to potentially learn more per-parameter if trained on a dataset of that language).
You can see a slideshow summary of the results here:
https://norod.github.io/wikicat-tokenizer-eval/tokenizer-slideshow.html
I hope I interpreted the results correctly, I've made the code available on GitHub so you can re-create the raw results jsonl with this repo:
https://github.com/Norod/wikicat-tokenizer-eval
Post on X:
https://x.com/Norod78/status/1984366900550266999
Analyzing 12 LLM Tokenizers Across 204 Languages.
First, I've created a dataset with Wikipedia's "Cat" article text in 272 languages:
Norod78/WikiCat-Multilingual
For each language entry with at least 100 words, I tokenized the text using 12 tokenizers and calculated the "Characters per token" ratio and "Word per token" ratio. The higher this ratio is, the more information each token represents on average for that language (and perhaps allowing the llm to potentially learn more per-parameter if trained on a dataset of that language).
You can see a slideshow summary of the results here:
https://norod.github.io/wikicat-tokenizer-eval/tokenizer-slideshow.html
I hope I interpreted the results correctly, I've made the code available on GitHub so you can re-create the raw results jsonl with this repo:
https://github.com/Norod/wikicat-tokenizer-eval
Post on X:
https://x.com/Norod78/status/1984366900550266999
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I am dedicating this weekend to practicing/reading the latest b(ook)log from hugging face. It is meant to be a guide for anyone trying to go from “we have a great dataset and GPUs” to “we built a really strong model.” Will share thoughts upon completion.
Thanks for the treat @eliebak @ThomasWolf and HF team!
HuggingFaceTB/smol-training-playbook
Thanks for the treat @eliebak @ThomasWolf and HF team!
HuggingFaceTB/smol-training-playbook
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🚀💡🌟 New Research Alert - ICCV 2025 (Oral)! 🌟🪄🚀
📄 Title: LoftUp: Learning a Coordinate-based Feature Upsampler for Vision Foundation Models 🔝
📝 Description: LoftUp is a coordinate-based transformer that upscales the low-resolution features of VFMs (e.g. DINOv2 and CLIP) using cross-attention and self-distilled pseudo-ground truth (pseudo-GT) from SAM.
👥 Authors: Haiwen Huang, Anpei Chen, Volodymyr Havrylov, Andreas Geiger, and Dan Zhang
📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸
📄 Paper: LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models (2504.14032)
🌐 Github Page: https://andrehuang.github.io/loftup-site
📁 Repository: https://github.com/andrehuang/loftup
🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
🚀 Added to the Foundation Models and Representation Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/foundation-models-and-representation-learning.md
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🔍 Keywords: #LoftUp #VisionFoundationModels #FeatureUpsampling #Cross-AttentionTransformer #CoordinateBasedLearning #SelfDistillation #PseudoGroundTruth #RepresentationLearning #AI #ICCV2025 #ResearchHighlight
📄 Title: LoftUp: Learning a Coordinate-based Feature Upsampler for Vision Foundation Models 🔝
📝 Description: LoftUp is a coordinate-based transformer that upscales the low-resolution features of VFMs (e.g. DINOv2 and CLIP) using cross-attention and self-distilled pseudo-ground truth (pseudo-GT) from SAM.
👥 Authors: Haiwen Huang, Anpei Chen, Volodymyr Havrylov, Andreas Geiger, and Dan Zhang
📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸
📄 Paper: LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models (2504.14032)
🌐 Github Page: https://andrehuang.github.io/loftup-site
📁 Repository: https://github.com/andrehuang/loftup
🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
🚀 Added to the Foundation Models and Representation Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/foundation-models-and-representation-learning.md
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🔍 Keywords: #LoftUp #VisionFoundationModels #FeatureUpsampling #Cross-AttentionTransformer #CoordinateBasedLearning #SelfDistillation #PseudoGroundTruth #RepresentationLearning #AI #ICCV2025 #ResearchHighlight
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