💫 F-COREF: Fast, Accurate and Easy to Use Coreference Resolution
a python package for fast, accurate, and easy-to-use English coreference resolution.
Github: https://github.com/shon-otmazgin/fastcoref
Paper: https://arxiv.org/abs/2209.04280v2
Dataset: https://paperswithcode.com/dataset/multi-news
@ArtificialIntelligencedl
a python package for fast, accurate, and easy-to-use English coreference resolution.
pip install fastcorefGithub: https://github.com/shon-otmazgin/fastcoref
Paper: https://arxiv.org/abs/2209.04280v2
Dataset: https://paperswithcode.com/dataset/multi-news
@ArtificialIntelligencedl
👍4
🦾 StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation
Github: https://github.com/adymaharana/storydalle
Paper: https://arxiv.org/abs/2209.06192
Model: https://github.com/adymaharana/storydalle/blob/main/MODEL_CARD.MD
Demo: https://github.com/adymaharana/storydalle/blob/main/DEMO.MD
Dataset: https://paperswithcode.com/dataset/multi-news
@ArtificialIntelligencedl
Github: https://github.com/adymaharana/storydalle
Paper: https://arxiv.org/abs/2209.06192
Model: https://github.com/adymaharana/storydalle/blob/main/MODEL_CARD.MD
Demo: https://github.com/adymaharana/storydalle/blob/main/DEMO.MD
Dataset: https://paperswithcode.com/dataset/multi-news
@ArtificialIntelligencedl
👍8🔥1
🚀 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: http://manikvarma.org/downloads/XC/XMLRepository.html
@ArtificialIntelligencedl
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: http://manikvarma.org/downloads/XC/XMLRepository.html
@ArtificialIntelligencedl
👍6
🛠 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
Github: https://github.com/microsoft/xpretrain
Paper: https://arxiv.org/abs/2209.06430v1
Dataset: https://paperswithcode.com/dataset/flickr30k
@ArtificialIntelligencedl
🔥7
📲 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
Github: https://github.com/ZM-Zhou/SMDE-Pytorch
Paper: https://arxiv.org/abs/2209.07088v1
Dataset: https://paperswithcode.com/dataset/cityscapes
@ArtificialIntelligencedl
👍6
🛠 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
⚙️Github: https://github.com/VITA-Group/Simple3D-Former
📄Paper: https://arxiv.org/abs/2209.07026v1
📎Dataset: https://paperswithcode.com/dataset/modelnet
@ArtificialIntelligencedl
👍6
🖊 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.
⚙️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
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
👍4
🔌 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.
⚙️Github: https://github.com/panagiotisanagnostou/hipart
📄Paper: https://arxiv.org/abs/2209.08680v1
📎Dataset: https://paperswithcode.com/dataset/usps
@ArtificialIntelligencedl
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
👍4👎2❤1🔥1
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
⚙️Github: https://github.com/gagolews/clustering-benchmarks
📄Paper: https://arxiv.org/abs/2209.09493v1
📎Results: https://github.com/gagolews/clustering-results-v1
@ArtificialIntelligencedl
👍8❤1🔥1
🎼 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
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
👍6❤1🔥1
Forwarded from Machinelearning
🗣 Robust Speech Recognition via Large-Scale Weak Supervision
Whisper is a general-purpose speech recognition model by Open AI.
⚙️ Github
💡 Colab
💻 Model
🗒 Paper
🦾 Dataset
✴️ HABR
@ai_machinelearning_big_data
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
👍5🔥2🥰1
🦾 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.
⚙️Github: https://github.com/deyingk/personalizedhandmeshestimation
📄Paper: https://arxiv.org/abs/2209.10840v1
🗒Dataset: https://paperswithcode.com/dataset/dexycb
@ArtificialIntelligencedl
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
👍4❤1🔥1
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline
⚙️Github: https://github.com/walker-hyf/mntts
📄Paper: https://arxiv.org/abs/2209.10848v1
🗒Dataset: https://paperswithcode.com/dataset/ljspeech
@ArtificialIntelligencedl
# 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
👍6❤1🔥1
🔸 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
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
👍7❤1🔥1
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🚀 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
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
👍7❤1🔥1
🦾 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
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
👍6❤1🔥1😁1
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
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
👍3❤1🔥1
🦾 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
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
👍4❤1🔥1
📰 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
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
👍8❤1🔥1
📌 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
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
👍8❤2🥰1
✔️ 4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation
⚙️Github: https://github.com/larskreuzberg/4d-stop
📄Paper: https://arxiv.org/abs/2209.14858v1
🗒Dataset: https://paperswithcode.com/dataset/semantickitti
@ArtificialIntelligencedl
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
👍6🤔2❤1🔥1