Deep Metric Learning for Unsupervised CD
🖥 Github: https://github.com/wgcban/metric-cd
⏩ Paper: https://arxiv.org/abs/2303.09536v1
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/wgcban/metric-cd
⏩ Paper: https://arxiv.org/abs/2303.09536v1
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⚜️ ViperGPT: Visual Inference via Python Execution for Reasoning
ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.
🖥 Github: https://github.com/cvlab-columbia/viper
⏩ Paper: https://arxiv.org/pdf/2303.08128.pdf
💨 Project: https://paperswithcode.com/dataset/beat
https://news.1rj.ru/str/DataScienceT
ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.
🖥 Github: https://github.com/cvlab-columbia/viper
⏩ Paper: https://arxiv.org/pdf/2303.08128.pdf
💨 Project: https://paperswithcode.com/dataset/beat
https://news.1rj.ru/str/DataScienceT
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🎥 Zero-1-to-3: Zero-shot One Image to 3D Object
Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.
🖥 Github: https://github.com/cvlab-columbia/zero123
🤗 Hugging face: https://huggingface.co/spaces/cvlab/zero123-live
⏩ Paper: https://arxiv.org/abs/2303.11328v1
⏩ Dataset: https://zero123.cs.columbia.edu/
💨 Project: https://paperswithcode.com/dataset/beat
⭐️ Demo: https://huggingface.co/spaces/cvlab/zero123
https://news.1rj.ru/str/DataScienceT
Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.
🖥 Github: https://github.com/cvlab-columbia/zero123
🤗 Hugging face: https://huggingface.co/spaces/cvlab/zero123-live
⏩ Paper: https://arxiv.org/abs/2303.11328v1
⏩ Dataset: https://zero123.cs.columbia.edu/
💨 Project: https://paperswithcode.com/dataset/beat
⭐️ Demo: https://huggingface.co/spaces/cvlab/zero123
https://news.1rj.ru/str/DataScienceT
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MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications.
2023 lectures are starting in just one day, Jan 9th!
Link to register:
http://introtodeeplearning.com
MIT Introduction to Deep Learning The 2022 lectures can be found here:
https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
https://news.1rj.ru/str/DataScienceT
2023 lectures are starting in just one day, Jan 9th!
Link to register:
http://introtodeeplearning.com
MIT Introduction to Deep Learning The 2022 lectures can be found here:
https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
https://news.1rj.ru/str/DataScienceT
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Train your ControlNet with diffusers 🧨
ControlNet is a neural network structure that allows fine-grained control of diffusion models by adding extra conditions.
🤗 Hugging face: https://huggingface.co/blog/train-your-controlnet#
🖥 Github: https://github.com/huggingface/blog/blob/main/train-your-controlnet.md
⏩ ControlNet training example: https://github.com/huggingface/diffusers/tree/main/examples/controlnet
https://news.1rj.ru/str/DataScienceT
ControlNet is a neural network structure that allows fine-grained control of diffusion models by adding extra conditions.
🤗 Hugging face: https://huggingface.co/blog/train-your-controlnet#
🖥 Github: https://github.com/huggingface/blog/blob/main/train-your-controlnet.md
⏩ ControlNet training example: https://github.com/huggingface/diffusers/tree/main/examples/controlnet
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🔥 Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
A new approach for high-quality domain translation with better controllability.
🖥 Github: https://github.com/LeeDongYeun/FixNoise
⏩ Paper: https://arxiv.org/abs/2303.11545v1
💨 Dataset: https://paperswithcode.com/dataset/metfaces
https://news.1rj.ru/str/DataScienceT
A new approach for high-quality domain translation with better controllability.
🖥 Github: https://github.com/LeeDongYeun/FixNoise
⏩ Paper: https://arxiv.org/abs/2303.11545v1
💨 Dataset: https://paperswithcode.com/dataset/metfaces
https://news.1rj.ru/str/DataScienceT
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"A panda is playing guitar on times square"
Text2Video-Zero
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Paper: https://arxiv.org/abs/2303.13439
Video Result: video result link
Source code: https://github.com/picsart-ai-research/text2video-zero
https://news.1rj.ru/str/DataScienceT
Text2Video-Zero
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Paper: https://arxiv.org/abs/2303.13439
Video Result: video result link
Source code: https://github.com/picsart-ai-research/text2video-zero
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Conditional Image-to-Video Generation with Latent Flow Diffusion Models
New approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image.
🖥 Github: https://github.com/nihaomiao/cvpr23_lfdm
⏩ Paper: https://arxiv.org/abs/2303.13744v1
💨 Dataset: https://drive.google.com/file/d/1dRn1wl5TUaZJiiDpIQADt1JJ0_q36MVG/view?usp=share_link
https://news.1rj.ru/str/DataScienceT
New approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image.
🖥 Github: https://github.com/nihaomiao/cvpr23_lfdm
⏩ Paper: https://arxiv.org/abs/2303.13744v1
💨 Dataset: https://drive.google.com/file/d/1dRn1wl5TUaZJiiDpIQADt1JJ0_q36MVG/view?usp=share_link
https://news.1rj.ru/str/DataScienceT
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Test of Time: Instilling Video-Language Models with a Sense of Time
GPT-5 will likely have video abilities, but will it have a sense of time? Here is answer to this question in #CVPR2023 paper by student of University of Amsterdam to learn how to instil time into video-language foundation models.
Paper:
https://arxiv.org/abs/2301.02074
Code:
https://github.com/bpiyush/TestOfTime
Project Page:
https://bpiyush.github.io/testoftime-website/
https://news.1rj.ru/str/DataScienceT
GPT-5 will likely have video abilities, but will it have a sense of time? Here is answer to this question in #CVPR2023 paper by student of University of Amsterdam to learn how to instil time into video-language foundation models.
Paper:
https://arxiv.org/abs/2301.02074
Code:
https://github.com/bpiyush/TestOfTime
Project Page:
https://bpiyush.github.io/testoftime-website/
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One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer
🖥 Github: https://github.com/IDEA-Research/OSX
⏩ Paper: http://arxiv.org/abs/2303.16160
⭐️ Project: https://osx-ubody.github.io
💨 Dataset: https://paperswithcode.com/dataset/expose
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/IDEA-Research/OSX
⏩ Paper: http://arxiv.org/abs/2303.16160
⭐️ Project: https://osx-ubody.github.io
💨 Dataset: https://paperswithcode.com/dataset/expose
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ViperGPT: Visual Inference via Python Execution for Reasoning
ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.
Github:
https://github.com/cvlab-columbia/viper
Paper:
https://arxiv.org/pdf/2303.08128.pdf
Project:
https://paperswithcode.com/dataset/beat
https://news.1rj.ru/str/DataScienceT
ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.
Github:
https://github.com/cvlab-columbia/viper
Paper:
https://arxiv.org/pdf/2303.08128.pdf
Project:
https://paperswithcode.com/dataset/beat
https://news.1rj.ru/str/DataScienceT
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WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research
Propose a three-stage processing pipeline for filtering noisy data and generating high-quality captions, where ChatGPT.
🖥 Github: https://github.com/xinhaomei/wavcaps
⏩ Paper: https://arxiv.org/abs/2303.17395v1
💨 Dataset: https://paperswithcode.com/dataset/sounddescs
https://news.1rj.ru/str/DataScienceT
Propose a three-stage processing pipeline for filtering noisy data and generating high-quality captions, where ChatGPT.
🖥 Github: https://github.com/xinhaomei/wavcaps
⏩ Paper: https://arxiv.org/abs/2303.17395v1
💨 Dataset: https://paperswithcode.com/dataset/sounddescs
https://news.1rj.ru/str/DataScienceT
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DPF: Learning Dense Prediction Fields with Weak Supervision
🖥 Github: https://github.com/cxx226/dpf
⏩ Paper: https://arxiv.org/abs/2303.16890v1
💨 Dataset: https://paperswithcode.com/dataset/pascal-context
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/cxx226/dpf
⏩ Paper: https://arxiv.org/abs/2303.16890v1
💨 Dataset: https://paperswithcode.com/dataset/pascal-context
https://news.1rj.ru/str/DataScienceT
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Human Guided Ground-truth Generation for Realistic Image Super-resolution
🖥 Github: https://github.com/chrisdud0257/hggt
⏩ Paper: https://arxiv.org/abs/2303.13069
💨 Dataset: https://paperswithcode.com/dataset/div2k
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/chrisdud0257/hggt
⏩ Paper: https://arxiv.org/abs/2303.13069
💨 Dataset: https://paperswithcode.com/dataset/div2k
https://news.1rj.ru/str/DataScienceT
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ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing
🖥 Github: https://github.com/alibaba/easyrobust/tree/main/benchmarks/imagenet-e
⏩ Paper: https://arxiv.org/abs/2303.17096v1
💨 Dataset: https://paperswithcode.com/dataset/objectnet
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/alibaba/easyrobust/tree/main/benchmarks/imagenet-e
⏩ Paper: https://arxiv.org/abs/2303.17096v1
💨 Dataset: https://paperswithcode.com/dataset/objectnet
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⚡️Token Merging for Stable Diffusion
Token Merging (ToMe) speeds up transformers by merging redundant tokens, which means the transformer has to do less work.
🖥 Github: https://github.com/dbolya/tomesd
⏩ Paper: https://arxiv.org/abs/2303.17604v1
💨 Blog: https://research.facebook.com/blog/2023/2/token-merging-your-vit-but-faster/
https://news.1rj.ru/str/DataScienceT
Token Merging (ToMe) speeds up transformers by merging redundant tokens, which means the transformer has to do less work.
pip install tomesd🖥 Github: https://github.com/dbolya/tomesd
⏩ Paper: https://arxiv.org/abs/2303.17604v1
💨 Blog: https://research.facebook.com/blog/2023/2/token-merging-your-vit-but-faster/
https://news.1rj.ru/str/DataScienceT
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⭐️ HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace
Language serves as an interface for LLMs to connect numerous AI models for solving complicated AI tasks!
🖥 Github: https://github.com/microsoft/JARVIS
⏩ Paper: https://arxiv.org/abs/2303.17604v1
https://news.1rj.ru/str/DataScienceT
Language serves as an interface for LLMs to connect numerous AI models for solving complicated AI tasks!
🖥 Github: https://github.com/microsoft/JARVIS
⏩ Paper: https://arxiv.org/abs/2303.17604v1
https://news.1rj.ru/str/DataScienceT
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WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
🖥 Github: https://github.com/hustvl/weaktr
⏩ Paper: https://arxiv.org/abs/2304.01184v1
💨 Dataset: https://paperswithcode.com/dataset/imagenet
https://news.1rj.ru/str/DataScienceT
🖥 Github: https://github.com/hustvl/weaktr
⏩ Paper: https://arxiv.org/abs/2304.01184v1
💨 Dataset: https://paperswithcode.com/dataset/imagenet
https://news.1rj.ru/str/DataScienceT
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