✨DINOv3

📝 Summary:
DINOv3 is a self-supervised vision model excelling across tasks. It scales datasets, prevents dense feature degradation via Gram anchoring, and uses post-hoc strategies for flexibility. This versatile foundation model outperforms specialized state of the art without fine-tuning.

🔹 Publication Date: Published on Aug 13

🔹 Paper Links:
• arXiv Page: https://huggingface.co/collections/facebook/dinov3
• PDF: https://arxiv.org/pdf/2508.10104
• Project Page: https://ai.meta.com/blog/dinov3-self-supervised-vision-model/
• Github: https://github.com/facebookresearch/dinov3

🔹 Models citing this paper:
• https://huggingface.co/facebook/dinov3-vit7b16-pretrain-lvd1689m
• https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m
• https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m

✨ Datasets citing this paper:
• https://huggingface.co/datasets/zhuangzhe1229/test_dataset
• https://huggingface.co/datasets/simon123905/vitl

✨ Spaces citing this paper:
• https://huggingface.co/spaces/atalaydenknalbant/DINOv3
• https://huggingface.co/spaces/manu02/DINOv3-Interactive-Patch-Cosine-Similarity
• https://huggingface.co/spaces/merve/dinov3-viz

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#DINOv3 #SelfSupervisedLearning #ComputerVision #FoundationModels #AI

🤖🧠 Concerto: How Joint 2D-3D Self-Supervised Learning Is Redefining Spatial Intelligence

🗓️ 09 Nov 2025
📚 AI News & Trends

The world of artificial intelligence is rapidly evolving and self-supervised learning has become a driving force behind breakthroughs in computer vision and 3D scene understanding. Traditional supervised learning relies heavily on labeled datasets which are expensive and time-consuming to produce. Self-supervised learning, on the other hand, extracts meaningful patterns without manual labels allowing models to ...

#SelfSupervisedLearning #ComputerVision #3DSceneUnderstanding #SpatialIntelligence #AIResearch #DeepLearning

✨VideoSSR: Video Self-Supervised Reinforcement Learning

📝 Summary:
VideoSSR is a novel self-supervised reinforcement learning framework that leverages intrinsic video information to generate high-quality training data. It uses three pretext tasks and the VideoSSR-30K dataset, improving MLLM performance across 17 benchmarks by over 5%.

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06281
• PDF: https://arxiv.org/pdf/2511.06281
• Project Page: https://github.com/lcqysl/VideoSSR
• Github: https://github.com/lcqysl/VideoSSR

🔹 Models citing this paper:
• https://huggingface.co/yhx12/VideoSSR

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#ReinforcementLearning #SelfSupervisedLearning #VideoAI #MachineLearning #DeepLearning

✨OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation

📝 Summary:
OlmoEarth is a novel multimodal spatio-temporal foundation model for Earth observation data. It employs new self-supervised learning methods to achieve state-of-the-art performance on many tasks. It is deployed as a platform for non-profits and NGOs.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13655
• PDF: https://arxiv.org/pdf/2511.13655
• Project Page: https://olmoearth.allenai.org/
• Github: https://github.com/allenai/olmoearth_pretrain

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#EarthObservation #FoundationModels #AI #RemoteSensing #SelfSupervisedLearning

✨UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity

📝 Summary:
UnSAMv2 enables continuous segmentation granularity control for the SAM model without human annotations. It uses self-supervised learning on unlabeled data to discover mask-granularity pairs and a novel control embedding. UnSAMv2 significantly enhances SAM-2s performance across various segmentati...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13714
• PDF: https://arxiv.org/pdf/2511.13714
• Project Page: https://yujunwei04.github.io/UnSAMv2-Project-Page/
• Github: https://github.com/yujunwei04/UnSAMv2

✨ Spaces citing this paper:
• https://huggingface.co/spaces/yujunwei04/UnSAMv2

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#AI #ComputerVision #SelfSupervisedLearning #ImageSegmentation #DeepLearning

✨Φeat: Physically-Grounded Feature Representation

📝 Summary:
Φeat is a new self-supervised visual backbone that captures material identity like reflectance and mesostructure. It learns robust features invariant to external physical factors such as shape and lighting, promoting physics-aware perception.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11270
• PDF: https://arxiv.org/pdf/2511.11270

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#ComputerVision #SelfSupervisedLearning #DeepLearning #FeatureLearning #PhysicsAwareAI