Count anything

An empirical study on few-shot counting using segment anything

🖥 Github: https://github.com/vision-intelligence-and-robots-group/count-anything

⏩ Paper: https://arxiv.org/abs/2304.10817v1

🤗 Hugging face: https://huggingface.co/spaces/nebula/counting-anything

📌 Dataset: https://drive.google.com/file/d/1ymDYrGs9DSRicfZbSCDiOu0ikGDh5k6S/view?usp=sharing

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🔍 Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System

Self-Controlled Memory (SCM) system to unleash infinite-length input capacity for large-scale language models.

🖥 Github: https://github.com/toufunao/SCM4LLMs

⏩ Paper: https://arxiv.org/abs/2304.13343v1

📌 Tasks: https://paperswithcode.com/task/language-modelling

https://t.iss.one/DataScienceT

🖌 Edit Everything: A Text-Guided Generative System for Images Editing

A text-guided generative system without any finetuning (zero-shot).

🖥 Github: https://github.com/defengxie/edit_everything

⏩ Paper: https://arxiv.org/abs/2304.14006v1

🚀 Dataset: https://paperswithcode.com/dataset/wukong

https://t.iss.one/DataScienceT

🖥 Awesome Chatgpt

Awesome list for ChatGPT — an artificial intelligence chatbot

🖥 Github: https://github.com/sindresorhus/awesome-chatgpt

💨 Examples: https://github.com/xiaowuc2/ChatGPT-Python-Applications

✅️ QuickGPT: https://sindresorhus.gumroad.com/l/quickgpt

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Local Topological Profile (LTP)

🖥 Github: https://github.com/j-adamczyk/ltp

⏩ Paper: https://arxiv.org/pdf/2305.00724v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/reddit

https://t.iss.one/DataScienceT

TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene Understanding

🖥 Github: https://github.com/prismformore/multi-task-transformer

⏩ Paper: https://openreview.net/pdf?id=-CwPopPJda

💨 Dataset: https://paperswithcode.com/dataset/cityscapes-3d

https://t.iss.one/DataScienceT

➡️ Prompt Tuning for Generative Multimodal Pretrained Models

Github: https://github.com/ofa-sys/ofa

Paper: https://arxiv.org/abs/2208.02532v1

Dataset: https://paperswithcode.com/dataset/snli-ve

Demo: https://huggingface.co/spaces/OFA-Sys/OFA-Generic_Interface

https://t.iss.one/DataScienceT

🦠 Learning Protein Representations via Complete 3D Graph Networks

DIG: Dive into Graphs is a turnkey library for graph deep learning research.

Github: https://github.com/divelab/DIG

Paper: https://arxiv.org/abs/2207.12600v1

Tutorials: https://diveintographs.readthedocs.io/en/latest/tutorials/graphdf.html

Documentation: https://diveintographs.readthedocs.io/

Benchmarks: https://github.com/divelab/DIG/tree/dig-stable/benchmarks

Dataset: https://paperswithcode.com/dataset/atom3d

https://t.iss.one/DataScienceT

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ZipIt! Merging Models from Different Tasks without Training

ZipIt allows to combine completely distinct models with different initializations, each solving a separate task, into one multi-task model without any additional training.

🖥 Github: https://github.com/gstoica27/zipit

⏩ Paper: https://arxiv.org/abs/2305.03053v1

📌 Dataset: https://paperswithcode.com/dataset/nabirds

https://t.iss.one/DataScienceT

🔈Text-to-Video: The Task, Challenges and the Current State

In this post, we covered the constraints, unique challenges and the current state of text-to-video generation models

🤗 Hugging face: https://huggingface.co/blog/text-to-video

🖥 Github: https://github.com/huggingface/blog/blob/main/text-to-video.md

⏩ Damo-vilab: https://huggingface.co/damo-vilab

📌 Dataset: https://m-bain.github.io/webvid-dataset/

https://t.iss.one/DataScienceT

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