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Artificial Intelligence l l AI Updates

Artificial Intelligence l l AI Updates

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πŸ”— GitHub_Link ❇️ Fine-tuning Florence-2 - Microsoft's Cutting-edge Vision Language Models #VLMs Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ Data processing with ML and LLM πŸ”₯ #LLMs Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence
πŸ”— GitHub_Link ❇️ Data processing with ML and LLM πŸ”₯ #LLMs Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ VIA: A Spatiotemporal Video Adaptation Framework for Global and Local Video Editing πŸ”₯ #VideoEditing Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ Awesome Evaluation of Visual Generation πŸ”₯ Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligen
πŸ”— GitHub_Link ❇️ Awesome Evaluation of Visual Generation πŸ”₯ Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link πŸ”— Blog_Link NVIDIA introduces DoRA (Weight-Decomposed Low-Rank Adaptation), a high-performing alternative to
πŸ”— GitHub_Link πŸ”— Blog_Link NVIDIA introduces DoRA (Weight-Decomposed Low-Rank Adaptation), a high-performing alternative to LoRA for fine-tuning. Key highlights: - DoRA consistently outperforms LoRA across various tasks, including commonsense reasoning, multi-turn benchmarks, and image-text understanding. - It improves both learning capacity and stability without introducing additional inference overhead. - Excels in LLM, VLM, compressed LLM, and #diffusion model applications. - It's designed to work seamlessly with existing LoRA implementations DoRA decomposes #pretrained weights into magnitude and directional components, allowing for more nuanced fine-tuning. This method closely mimics full fine-tuning behavior while maintaining the parameter efficiency of LoRA. Hugging Face #developers can easily implement DoRA by setting 'use_dora=True' in their LoraConfig, making it readily accessible to improve LLM. Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ Odd-One-Out: Anomaly Detection by Comparing with Neighbors ⚑️ #AnomalyDetection Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link RT-DETR is now supported in Hugging Face Transformers! πŸ™Œ RT-DETR, short for β€œReal-Time DEtection TRansformer”, is a computer vision model developed at Peking University and Baidu, Inc. capable of real-time object detection. The authors claim better performance than YOLO models in both speed and accuracy. The model comes with an Apache 2.0 license, meaning people can freely use it for commercial applications. πŸ”₯ RT-DETR is a follow-up work of DETR, a model developed by AI at Meta that successfully used Transformers for the first time for object detection. The latter has been in the Transformers library since 2020. After this, lots of improvements have been made to enable faster convergence and inference speed. RT-DETR is an important example of that as it unlocks real-time inference at high accuracy! Big congrats to Daniel Choi for contributing this model! Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ Doduo: Dense Visual Correspondence from Unsupervised Semantic-Aware Flow #UnsupervisedLearning Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ GiT: the first successful LLM-like general vision model unifies various vision tasks only with a vanilla Vi
πŸ”— GitHub_Link ❇️ GiT: the first successful LLM-like general vision model unifies various vision tasks only with a vanilla ViT πŸ’₯ #LLMs #ViT Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ Large-Vocabulary Continuous Sign Language Recognition Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial
πŸ”— GitHub_Link ❇️ Large-Vocabulary Continuous Sign Language Recognition Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data #Robotics Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡
πŸ”— GitHub_Link ❇️ ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data #Robotics Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ OPTIMUS: Imitating Task and Motion Planning with Visuomotor Transformers #Robotics Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡οΏ½
πŸ”— GitHub_Link ❇️ OPTIMUS: Imitating Task and Motion Planning with Visuomotor Transformers #Robotics Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Gen
πŸ”— GitHub_Link ❇️ UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning #Robotics Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks #Robotics Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ Dex Retargeting: Various retargeting optimizers to translate human hand motion to robot hand motion #MotionRetargeting Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ Generative Region-Language Pretraining for Open-Ended Object Detection #ObjectDetection Join my channel: πŸ‘‡
πŸ”— GitHub_Link ❇️ Generative Region-Language Pretraining for Open-Ended Object Detection #ObjectDetection Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild #NeRF Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration Join my
πŸ”— GitHub_Link ❇️ From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects #3D #ObjectDetection Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates

πŸ”— GitHub_Link ❇️ AsyncDepth: Better Monocular 3D Detectors with LiDAR from the Past #LiDAR #Depth #3D Join my channel: πŸ‘‡πŸ‘‡οΏ½
πŸ”— GitHub_Link ❇️ AsyncDepth: Better Monocular 3D Detectors with LiDAR from the Past #LiDAR #Depth #3D Join my channel: πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ https://t.me/Artificial_Intelligence_Updates