💻 ACU - Awesome Agents for Computer Use

A project that contains a carefully selected list of resources about AI agents designed to run autonomously on your computers.

It includes research studies, projects, frameworks, guides and various tools.

Agents support task analysis and decision making functions for interacting with any interface.

▪️ Github

#aiagents #awesome #agents

https://news.1rj.ru/str/DataScienceT ✅

LatentSync: Audio Conditioned Latent Diffusion Models for Lip Sync

Paper: https://arxiv.org/pdf/2412.09262v1.pdf

Code: https://github.com/bytedance/LatentSync

https://news.1rj.ru/str/DataScienceT 💎

KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation

Paper: https://arxiv.org/pdf/2409.13731v3.pdf

Code: https://github.com/openspg/kag

Datasets: 2WikiMultiHopQA

https://news.1rj.ru/str/DataScienceT 🎁

OpenHands: An Open Platform for AI Software Developers as Generalist Agents

paper: https://arxiv.org/pdf/2407.16741v2.pdf

Code:
https://github.com/opendevin/opendevin
https://github.com/all-hands-ai/openhands

https://news.1rj.ru/str/DataScienceT ❤️

Story-Adapter: A Training-free Iterative Framework for Long Story Visualization

Paper: https://arxiv.org/pdf/2410.06244v1.pdf

Code: https://github.com/jwmao1/story-adapter

https://news.1rj.ru/str/DataScienceT 📊

Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations

Paper: https://arxiv.org/pdf/2408.15232v2.pdf

Code: https://github.com/stanford-oval/storm

https://news.1rj.ru/str/DataScienceT ⚠️

Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction

Paper: https://arxiv.org/pdf/2501.03218v1.pdf

Code: https://github.com/mark12ding/dispider

Dataset: Video-MME

https://news.1rj.ru/str/DataScienceT 🎙

SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models

Paper: https://arxiv.org/pdf/2412.11058v1.pdf

Code:
https://github.com/snowfallingplum/shmt
https://github.com/snowfallingplum/csd-mt

https://news.1rj.ru/str/DataScienceT 🐍#️⃣

AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans

Paper: https://arxiv.org/pdf/2407.02418v2.pdf

Code: https://github.com/GabrieleLozupone/AXIAL

Dataset: ADNI

https://news.1rj.ru/str/DataScienceT 🧠

FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors

Paper: https://arxiv.org/pdf/2501.08225v1.pdf

Code: https://github.com/ybybzhang/framepainter

https://news.1rj.ru/str/DataScienceT ✈️

Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget

Paper: https://arxiv.org/pdf/2407.15811v1.pdf

code: https://github.com/sonyresearch/micro_diffusion

Datasets: MS COCO

https://news.1rj.ru/str/DataScienceT 🧠

MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation

Paper: https://arxiv.org/pdf/2501.06713v2.pdf

Code: https://github.com/hkuds/minirag

https://news.1rj.ru/str/DataScienceT 🧠

Continual Forgetting for Pre-trained Vision Models (CVPR2024)

🖥 Github: https://github.com/bjzhb666/GS-LoRA

📕 Paper: https://arxiv.org/abs/2501.09705v1

🧠 Dataset: https://paperswithcode.com/dataset/coco

https://news.1rj.ru/str/DataScienceT 🧠

Tensor Product Attention Is All You Need

Paper: https://arxiv.org/pdf/2501.06425v1.pdf

Code: https://github.com/tensorgi/t6

Dataset: MMLU

https://news.1rj.ru/str/DataScienceT ✅

UnCommon Objects in 3D

We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360 coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications.

Paper: https://arxiv.org/pdf/2501.07574v1.pdf

Code: https://github.com/facebookresearch/uco3d

DataSet: MS COCO

https://news.1rj.ru/str/DataScienceT 🐻‍❄️