🚀 Entity Tagging: Extracting Entities in Text Without Mention Supervision

Github: https://github.com/facebookresearch/groov

Paper: https://arxiv.org/abs/2209.06148v1

Model: https://github.com/adymaharana/storydalle/blob/main/MODEL_CARD.MD

Dataset: https://manikvarma.org/downloads/XC/XMLRepository.html

@ArtificialIntelligencedl

🛠 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

@ArtificialIntelligencedl

📲 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

@ArtificialIntelligencedl

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

⚙️Github: https://github.com/VITA-Group/Simple3D-Former

📄Paper: https://arxiv.org/abs/2209.07026v1

📎Dataset: https://paperswithcode.com/dataset/modelnet

@ArtificialIntelligencedl

🖊 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

⚙️Github: https://github.com/mmasana/FACIL

📄Paper: https://arxiv.org/abs/2209.08010v1

📎Dataset: https://github.com/mmasana/FACIL/blob/master/src/datasets#datasets

@ArtificialIntelligencedl

🔌 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

⚙️Github: https://github.com/panagiotisanagnostou/hipart

📄Paper: https://arxiv.org/abs/2209.08680v1

📎Dataset: https://paperswithcode.com/dataset/usps

@ArtificialIntelligencedl

A Framework for Benchmarking Clustering Algorithms

⚙️Github: https://github.com/gagolews/clustering-benchmarks

📄Paper: https://arxiv.org/abs/2209.09493v1

📎Results: https://github.com/gagolews/clustering-results-v1

@ArtificialIntelligencedl

🎼 A Framework for Benchmarking Clustering Algorithms

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

⚙️Github: https://github.com/megvii-basedetection/bevstereo

📄Paper: https://arxiv.org/abs/2209.10248v1

🗒Dataset: https://paperswithcode.com/dataset/nuscenes

@ArtificialIntelligencedl

🗣 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

⚙️ Github
💡 Colab
💻 Model
🗒 Paper
🦾 Dataset
✴️ HABR

@ai_machinelearning_big_data

🦾 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

⚙️Github: https://github.com/deyingk/personalizedhandmeshestimation

📄Paper: https://arxiv.org/abs/2209.10840v1

🗒Dataset: https://paperswithcode.com/dataset/dexycb

@ArtificialIntelligencedl

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

⚙️Github: https://github.com/walker-hyf/mntts

📄Paper: https://arxiv.org/abs/2209.10848v1

🗒Dataset: https://paperswithcode.com/dataset/ljspeech

@ArtificialIntelligencedl

🔸 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.

⚙️Github: https://github.com/newbeeer/poisson_flow

📄Paper: https://arxiv.org/abs/2209.11178v1

🗒Dataset: https://paperswithcode.com/dataset/lsun

@ArtificialIntelligencedl

🚀 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

⚙️Github: https://github.com/liuruoze/mini-AlphaStar

📄Paper: https://arxiv.org/abs/2209.11553v1

🗒HierNet-SC2: https://github.com/liuruoze/hiernet-sc2

@ArtificialIntelligencedl

🦾 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.

⚙️Github: https://github.com/alibaba/easyrec

📄Paper: https://arxiv.org/abs/2209.12766v1

🗒Dataset: https://paperswithcode.com/dataset/criteo

@ArtificialIntelligencedl

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.

⚙️Github: https://github.com/tagoyal/factuality-datasets

📄Paper: https://arxiv.org/abs/2209.12356v1

🗒Dataset: https://paperswithcode.com/dataset/cnn-daily-mail-1

@ArtificialIntelligencedl

🦾 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.

⚙️Github: https://github.com/casia-iva-lab/obj2seq

📄Paper: https://arxiv.org/abs/2209.13948

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

@ArtificialIntelligencedl

📰 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.

⚙️Github: https://github.com/nicozwy/cofced

📄Paper: https://arxiv.org/abs/2209.14642v1

🗒Dataset: https://paperswithcode.com/dataset/fever

@ArtificialIntelligencedl

📌 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.

⚙️Github: https://github.com/1202kbs/dmcmc

📄Paper: https://arxiv.org/abs/2209.14593v1

🗒Dataset: https://paperswithcode.com/dataset/celeba-hq

@ArtificialIntelligencedl

✔️ 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

⚙️Github: https://github.com/larskreuzberg/4d-stop

📄Paper: https://arxiv.org/abs/2209.14858v1

🗒Dataset: https://paperswithcode.com/dataset/semantickitti

@ArtificialIntelligencedl

📌 ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing

An unsupervised end-to-end network for discovering sketch and extrude from point clouds.

conda create --name ExtrudeNet python=3.7
conda activate ExtrudeNet
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
conda install -c open3d-admin open3d
conda install numpy
conda install pymcubes
conda install tensorboard
conda install scipy
pip install tqdm

⚙️Github: https://github.com/kimren227/extrudenet

📄Paper: https://arxiv.org/abs/2209.15632v1

@ArtificialIntelligencedl