AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning, #MachineLearning, LLMs and #ComputerVision

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/

#artificialintelligence #machinelearning #ml #AI
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๐Ÿฆ  Segment & Track Any Cell ๐Ÿฆ 

๐Ÿ‘‰RWTH unveils a novel zero-shot cell tracking framework by integrating Segment Anything 2 (SAM2) into the tracking pipeline. Source Code released๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/n_srg
๐Ÿ‘‰Paper https://arxiv.org/pdf/2509.09943
๐Ÿ‘‰Repo https://github.com/zhuchen96/sam4celltracking
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๐Ÿ”ฅ How We Use ChatGPT ๐Ÿ”ฅ

๐Ÿ‘‰By July 2025, ChatGPT has 700M+ users sending more than 2.5B+ messages per day. About 29,000 messages per second. This paper documents eight important facts about ChatGPT usage in the last three years. 63 pages of impressive statistics. To read.๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/QYHSi
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๐Ÿ›ก๏ธ3D Prompted Vision-LLM๐Ÿ›ก๏ธ

๐Ÿ‘‰#Nvidia unveils SR-3D, a novel aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. Flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without the need for exhaustive multi-frame labeling. Code & Dataset announced๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/5Y2c5
๐Ÿ‘‰Paper https://arxiv.org/pdf/2509.13317
๐Ÿ‘‰Project https://www.anjiecheng.me/sr3d
๐Ÿ‘‰Repo TBA
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๐Ÿ• Superpixel Anything (SOTA) ๐Ÿ•

๐Ÿ‘‰ SuperPixel Anything Model, a versatile framework for segmenting images. Extracting image features for superpixel generation blended with a large-scale pretrained model for semantic-agnostic segmentation to ensure superpixels alignement with masks. Damn romantic. Repo & Dataset available๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/rpxRh
๐Ÿ‘‰Paper arxiv.org/pdf/2509.12791
๐Ÿ‘‰Repo github.com/waldo-j/spam
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AI with Papers - Artificial Intelligence & Deep Learning pinned ยซIโ€™m keeping the channel free from interaction to avoid SPAM. The only way to interact is commenting the post after being accepted in the subchannel. Do you like this setting?ยป
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๐Ÿ‘ฝDAM for SAM2 Tracking๐Ÿ‘ฝ

๐Ÿ‘‰From the University of Ljubljana a novel distractor-aware drop-in memory module for SAM2. Reducing the tracking drift toward distractors and improves redetection capability after object occlusions. DAM4SAM outperforms SAM2.1, SOTA on 10 benchmarks. Repo released ๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/8aR59
๐Ÿ‘‰Paper https://arxiv.org/pdf/2509.13864
๐Ÿ‘‰Project jovanavidenovic.github.io/dam-4-sam/
๐Ÿ‘‰Repo github.com/jovanavidenovic/DAM4SAM
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๐Ÿ”ฅ๐Ÿ”ฅ It's time to decide whether you want to give LinkedIn your data for AI training or not ๐Ÿ”ฅ๐Ÿ”ฅ

Poll: https://lnkd.in/p/ddnenZgH

Set here: https://linkedin.com/mypreferences/d/settings/data-for-ai-improvement
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๐Ÿณ Invariant Saliency Detection ๐Ÿณ

๐Ÿ‘‰SI-SOD: invariant salient object detection in scenarios when multiple salient objects of significantly different sizes appear within a single image. Repo released๐Ÿ’™

๐Ÿ‘‰Review https://lnkd.in/p/dZBfbSsf
๐Ÿ‘‰Paper https://arxiv.org/pdf/2509.15573
๐Ÿ‘‰Project https://ferry-li.github.io/SI_SOD/
๐Ÿ‘‰Repo https://github.com/Ferry-Li/SI-SOD
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๐Ÿซ“ WINNER of LSVOS Challenge ๐Ÿซ“

๐Ÿ‘‰SaSaSa2VA introduces Segmentation Augmentation to improve global video understanding while remaining efficient, and employs Selective Averaging at inference to robustly fuse complementary predictions. This approach achieves SOTA on the 7th LSVOS Challenge (RVOS track). A practical solution with full repo under Apache๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/aH4mB
๐Ÿ‘‰Paper https://arxiv.org/pdf/2509.16972
๐Ÿ‘‰Repo https://github.com/magic-research/Sa2VA
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๐Ÿ†MOSEv2 Challenge Winner๐Ÿ†

๐Ÿ‘‰A practical solution for complex segmentation based on the Segment Concept (SeC), a concept-driven segmentation framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations. Repo under Apache 2.0๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/2MjNm
๐Ÿ‘‰Paper arxiv.org/pdf/2509.19183
๐Ÿ‘‰Paper (SeC) arxiv.org/pdf/2507.15852
๐Ÿ‘‰Repo github.com/OpenIXCLab/SeC
๐Ÿ‘‰Project rookiexiong7.github.io/projects/SeC/
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๐ŸŒ€ CLOPS: Vision-Driven Avatar ๐ŸŒ€

๐Ÿ‘‰CLOPS is the first human avatar solely uses egocentric vision to perceive its surroundings and navigate. CLOPS is able to realistically move in a scene and use egocentric vision in order to find a goal in a loop of visual perception & motion. Code announced๐Ÿ’™

๐Ÿ‘‰Review https://t.ly/RXp64
๐Ÿ‘‰Paper https://arxiv.org/pdf/2509.19259
๐Ÿ‘‰Project markos-diomataris.github.io/projects/clops/
๐Ÿ‘‰Repo TBA
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