Artificial Intelligence
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Artificial Intelligence

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πŸ’« F-COREF: Fast, Accurate and Easy to Use Coreference Resolution

a python package for fast, accurate, and easy-to-use English coreference resolution.

pip install fastcoref

Github: https://github.com/shon-otmazgin/fastcoref

Paper: https://arxiv.org/abs/2209.04280v2

Dataset: https://paperswithcode.com/dataset/multi-news

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πŸ›  CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment

Github: https://github.com/microsoft/xpretrain

Paper: https://arxiv.org/abs/2209.06430v1

Dataset: https://paperswithcode.com/dataset/flickr30k

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πŸ“² Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation

Github: https://github.com/ZM-Zhou/SMDE-Pytorch

Paper: https://arxiv.org/abs/2209.07088v1

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

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πŸ›  Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?

βš™οΈGithub: https://github.com/VITA-Group/Simple3D-Former

πŸ“„Paper: https://arxiv.org/abs/2209.07026v1

πŸ“ŽDataset: https://paperswithcode.com/dataset/modelnet

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πŸ–Š Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.

git clone https://github.com/mmasana/FACIL.git
cd FACIL


βš™οΈGithub: https://github.com/mmasana/FACIL

πŸ“„Paper: https://arxiv.org/abs/2209.08010v1

πŸ“ŽDataset: https://github.com/mmasana/FACIL/blob/master/src/datasets#datasets

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πŸ”Œ HiPart: Hierarchical divisive clustering toolbox

It is a package with similar execution principles as the scikit-learn package. It also provides two types of static visualizations for all the algorithms executed in the package, with the addition of linkage generation for the divisive hierarchical clustering structure.

pip install HiPart


βš™οΈGithub: https://github.com/panagiotisanagnostou/hipart

πŸ“„Paper: https://arxiv.org/abs/2209.08680v1

πŸ“ŽDataset: https://paperswithcode.com/dataset/usps

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🎼 A Framework for Benchmarking Clustering Algorithms

BEVStereo is a new multi-view 3D object detector using temporal stereo to enhance depth estimation.

βš™οΈGithub: https://github.com/megvii-basedetection/bevstereo

πŸ“„Paper: https://arxiv.org/abs/2209.10248v1

πŸ—’Dataset: https://paperswithcode.com/dataset/nuscenes

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Forwarded from Machinelearning
πŸ—£ Robust Speech Recognition via Large-Scale Weak Supervision

Whisper is a general-purpose speech recognition model by Open AI.

pip install git+https://github.com/openai/whisper.git

βš™οΈ Github
πŸ’‘ Colab
πŸ’» Model
πŸ—’ Paper
🦾 Dataset
✴️ HABR

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🦾 Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

A novel personalization pipeline to calibrate the intrinsic shape parameters using only a few unlabeled RGB images of the subject.

conda create -n IdHandMesh python=3.8
conda activate IdHandMesh


βš™οΈGithub: https://github.com/deyingk/personalizedhandmeshestimation

πŸ“„Paper: https://arxiv.org/abs/2209.10840v1

πŸ—’Dataset: https://paperswithcode.com/dataset/dexycb

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MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline

# Clone the repo
git clone https://github.com/walker-hyf/MnTTS.git
cd $PROJECT_ROOT_DIR

βš™οΈGithub: https://github.com/walker-hyf/mntts

πŸ“„Paper: https://arxiv.org/abs/2209.10848v1

πŸ—’Dataset: https://paperswithcode.com/dataset/ljspeech

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πŸ”Έ Poisson Flow Generative Models

A new Poisson flow generative model (PFGM) that maps a uniform distribution on a high-dimensional hemisphere into any data distribution.

βš™οΈGithub: https://github.com/newbeeer/poisson_flow

πŸ“„Paper: https://arxiv.org/abs/2209.11178v1

πŸ—’Dataset: https://paperswithcode.com/dataset/lsun

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πŸš€ On Efficient Reinforcement Learning for Full-length Game of StarCraft II

In this work, we investigate a set of RL techniques for the full-length game of StarCraft II

βš™οΈGithub: https://github.com/liuruoze/mini-AlphaStar

πŸ“„Paper: https://arxiv.org/abs/2209.11553v1

πŸ—’HierNet-SC2: https://github.com/liuruoze/hiernet-sc2

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🦾 EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems


EasyRec implements state of the art deep learning models used in common recommendation tasks: candidate generation(matching), scoring(ranking), and multi-task learning.

βš™οΈGithub: https://github.com/alibaba/easyrec

πŸ“„Paper: https://arxiv.org/abs/2209.12766v1

πŸ—’Dataset: https://paperswithcode.com/dataset/criteo

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News Summarization and Evaluation in the Era of GPT-3

Corpus of 10K generated summaries from fine-tuned and zero-shot models across 4 standard summarization benchmarks.


βš™οΈGithub: https://github.com/tagoyal/factuality-datasets

πŸ“„Paper: https://arxiv.org/abs/2209.12356v1

πŸ—’Dataset: https://paperswithcode.com/dataset/cnn-daily-mail-1

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🦾 Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks

An object-centric vision framework, Obj2Seq. Obj2Seq takes objects as basic units, and regards most object-level visual tasks as sequence generation problems of objects.

βš™οΈGithub: https://github.com/casia-iva-lab/obj2seq

πŸ“„Paper: https://arxiv.org/abs/2209.13948

πŸ—’Dataset: https://paperswithcode.com/dataset/coco

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πŸ“° A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection

a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones.

βš™οΈGithub: https://github.com/nicozwy/cofced

πŸ“„Paper: https://arxiv.org/abs/2209.14642v1

πŸ—’Dataset: https://paperswithcode.com/dataset/fever

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πŸ“Œ Denoising MCMC for Accelerating Diffusion-Based Generative Models

a general sampling framework, Denoising MCMC (DMCMC), that combines Markov chain Monte Carlo (MCMC) with reverse-SDE/ODE integrators / diffusion models to accelerate score-based sampling.

βš™οΈGithub: https://github.com/1202kbs/dmcmc

πŸ“„Paper: https://arxiv.org/abs/2209.14593v1

πŸ—’Dataset: https://paperswithcode.com/dataset/celeba-hq

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βœ”οΈ 4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation

conda create --name <env> --file requirements.txt

cd cpp_wrappers
sh compile_wrappers.sh

cd pointnet2
python setup.py install

βš™οΈGithub: https://github.com/larskreuzberg/4d-stop

πŸ“„Paper: https://arxiv.org/abs/2209.14858v1

πŸ—’Dataset: https://paperswithcode.com/dataset/semantickitti

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