♟ ChessVision - A dataset for logically coherent multi-label classification.


🖥 Github: https://github.com/wojciechkusa/systematic-review-datasets

📕 Paper: https://arxiv.org/pdf/2311.12474v1.pdf

✨ Tasks: https://paperswithcode.com/task/question-answering

🔥Datasets: https://paperswithcode.com/dataset/blurb

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GraphEmb

🖥 Github: https://github.com/ubioinformat/graphemb

📕 Paper: https://arxiv.org/pdf/2311.12670v1.pdf

✨ Tasks: https://paperswithcode.com/task/benchmarking

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Infant Action Recognition

🖥 Github: https://github.com/ostadabbas/video-based-infant-action-recognition

📕 Paper: https://arxiv.org/pdf/2311.12300v1.pdf

✨ Tasks: https://paperswithcode.com/task/action-recognition-in-videos

🔥Datasets: https://paperswithcode.com/dataset/ntu-rgb-d

CSMeD: Citation Screening Meta-Dataset for systematic review automation evaluation

🖥 Github: https://github.com/wojciechkusa/systematic-review-datasets

📕 Paper: https://arxiv.org/pdf/2311.12474v1.pdf

✨ Tasks: https://paperswithcode.com/task/question-answering

🔥Datasets: https://paperswithcode.com/dataset/blurb

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🔥 Seamless: Multilingual Expressive and Streaming Speech Translation

🔥Hugging face demo: https://huggingface.co/collections/facebook/seamless-communication-6568d486ef451c6ba62c7724

⚡️ BLog: https://ai.meta.com/blog/seamless-communication/

📚 Paper: https://scontent.fbkk5-5.fna.fbcdn.net

🌟 Demo: https://seamless.metademolab.com/expressive

🖥 Github: https://github.com/facebookresearch/seamless_communication

Probabilistic-Forecast-Reconciliation-with-DL

🖥 Github: https://github.com/guanyu0316/Probabilistic-Forecast-Reconciliation-with-DL

📕 Paper: https://arxiv.org/pdf/2311.12279v1.pdf

✨ Tasks: https://paperswithcode.com/task/time-series-1

🦾 StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

🖥 Github: https://github.com/yl4579/StyleTTS2

🔥Colab: https://colab.research.google.com/github/yl4579/StyleTTS2/blob/main/

⚡️ Demo: https://huggingface.co/spaces/styletts2/styletts2

📚 Paper: https://arxiv.org/abs/2306.07691

🌟 Demo: https://seamless.metademolab.com/expressive

🥩 Page: styletts2.github.io

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Towards Learning a Generalist Model for Embodied Navigation

🖥 Github: https://github.com/zd11024/NaviLLM

📕 Paper: https://arxiv.org/pdf/2312.02010v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/room-to-room

✨ Tasks: https://paperswithcode.com/task/3d-question-answering-3d-qa

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Paved2Paradise

🖥 Github: https://github.com/airalcorn2/paved2paradise

📕 Paper: https://arxiv.org/pdf/2312.01117v1.pdf

🔥Datasets: https://paperswithcode.com/dataset/kitti

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🖥 Hitchhiker's Guide to Python

Python Best Practices Guidebook

A guide to best practices for installing, configuring and using Python on a daily basis, including pip, numpy, virtualenv and more.

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Awesome Radar Perception

🖥 Github: https://github.com/Radar-Camera-Fusion/Awesome-Radar-Perception

📕 Paper: https://arxiv.org/pdf/2312.04861.pdf

✨ Tasks: https://paperswithcode.com/task/object-detection

📚 LlaVa Demo with LlamaIndex

In this example, we illustrate how we use LlaVa for belowing tasks:
▪Retrieval Augmented Image Captioning
▪Pydantic Structured Output
▪Multi-Modal Retrieval-Augmented Generation (RAG) using Llava-13b

🖥 Github: https://github.com/run-llama/llama_index/blob/main/docs/examples/multi_modal/llava_demo.ipynb

🔍 Colab: https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/multi_modal/llava_demo.ipynb

🪩 Paper: https://arxiv.org/abs/2304.08485

💻 Website: https://llava-vl.github.io/

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🌍 BioCLIP: A Vision Foundation Model for the Tree of Life

- paper: https://arxiv.org/abs/2311.18803
- project: https://imageomics.github.io/bioclip/
- demo: https://huggingface.co/spaces/imageomics/bioclip-demo
- model: https://huggingface.co/imageomics/bioclip

🏆 DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

🌐page: https://doubiiu.github.io/projects/DynamiCrafter
📄paper: https://arxiv.org/abs/2310.12190
🧬code: https://github.com/Doubiiu/DynamiCrafter
🕸replicate: https://replicate.com/camenduru/dynami-crafter

BatchER

🖥 Github: https://github.com/fmh1art/batcher

📕 Paper: https://arxiv.org/pdf/2312.03987v1.pdf

✨ Tasks: https://paperswithcode.com/task/data-integration