MassMIND: Massachusetts Maritime INfrared Dataset 1
Github: https://github.com/uml-marine-robotics/massmind
Paper: https://arxiv.org/abs/2209.04097v1
Dataset: https://drive.google.com/file/d/1T572f0oqy5JmuTvVEwkSUeXLWOSHl4hL/view
@ArtificialIntelligencedl
Github: https://github.com/uml-marine-robotics/massmind
Paper: https://arxiv.org/abs/2209.04097v1
Dataset: https://drive.google.com/file/d/1T572f0oqy5JmuTvVEwkSUeXLWOSHl4hL/view
@ArtificialIntelligencedl
π6
π« 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 fastcoref
Github: https://github.com/shon-otmazgin/fastcoref
Paper: https://arxiv.org/abs/2209.04280v2
Dataset: https://paperswithcode.com/dataset/multi-news
@ArtificialIntelligencedl
π4
π¦Ύ 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
π8π₯1
π 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
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
π6
π 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
π₯7
π² 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
π6
π Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?
βοΈGithub: https://github.com/VITA-Group/Simple3D-Former
πPaper: https://arxiv.org/abs/2209.07026v1
πDataset: https://paperswithcode.com/dataset/modelnet
@ArtificialIntelligencedl
βοΈGithub: https://github.com/VITA-Group/Simple3D-Former
πPaper: https://arxiv.org/abs/2209.07026v1
πDataset: https://paperswithcode.com/dataset/modelnet
@ArtificialIntelligencedl
π6
π 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.
βοΈGithub: https://github.com/mmasana/FACIL
πPaper: https://arxiv.org/abs/2209.08010v1
πDataset: 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
βοΈGithub: https://github.com/mmasana/FACIL
πPaper: https://arxiv.org/abs/2209.08010v1
πDataset: https://github.com/mmasana/FACIL/blob/master/src/datasets#datasets
@ArtificialIntelligencedl
π4
π 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.
βοΈGithub: https://github.com/panagiotisanagnostou/hipart
πPaper: https://arxiv.org/abs/2209.08680v1
πDataset: 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
βοΈGithub: https://github.com/panagiotisanagnostou/hipart
πPaper: https://arxiv.org/abs/2209.08680v1
πDataset: https://paperswithcode.com/dataset/usps
@ArtificialIntelligencedl
π4π2β€1π₯1
A Framework for Benchmarking Clustering Algorithms
βοΈGithub: https://github.com/gagolews/clustering-benchmarks
πPaper: https://arxiv.org/abs/2209.09493v1
πResults: https://github.com/gagolews/clustering-results-v1
@ArtificialIntelligencedl
βοΈGithub: https://github.com/gagolews/clustering-benchmarks
πPaper: https://arxiv.org/abs/2209.09493v1
πResults: https://github.com/gagolews/clustering-results-v1
@ArtificialIntelligencedl
π8β€1π₯1
πΌ A Framework for Benchmarking Clustering Algorithms
BEVStereo is a new multi-view 3D object detector using temporal stereo to enhance depth estimation.
βοΈGithub: https://github.com/megvii-basedetection/bevstereo
πPaper: https://arxiv.org/abs/2209.10248v1
πDataset: https://paperswithcode.com/dataset/nuscenes
@ArtificialIntelligencedl
BEVStereo is a new multi-view 3D object detector using temporal stereo to enhance depth estimation.
βοΈGithub: https://github.com/megvii-basedetection/bevstereo
πPaper: https://arxiv.org/abs/2209.10248v1
πDataset: https://paperswithcode.com/dataset/nuscenes
@ArtificialIntelligencedl
π6β€1π₯1
Forwarded from Machinelearning
π£ Robust Speech Recognition via Large-Scale Weak Supervision
Whisper is a general-purpose speech recognition model by Open AI.
βοΈ Github
π‘ Colab
π» Model
π Paper
π¦Ύ Dataset
β΄οΈ 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
βοΈ Github
π‘ Colab
π» Model
π Paper
π¦Ύ Dataset
β΄οΈ HABR
@ai_machinelearning_big_data
π5π₯2π₯°1
π¦Ύ 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.
βοΈGithub: https://github.com/deyingk/personalizedhandmeshestimation
πPaper: https://arxiv.org/abs/2209.10840v1
πDataset: 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
βοΈGithub: https://github.com/deyingk/personalizedhandmeshestimation
πPaper: https://arxiv.org/abs/2209.10840v1
πDataset: https://paperswithcode.com/dataset/dexycb
@ArtificialIntelligencedl
π4β€1π₯1
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline
βοΈGithub: https://github.com/walker-hyf/mntts
πPaper: https://arxiv.org/abs/2209.10848v1
πDataset: https://paperswithcode.com/dataset/ljspeech
@ArtificialIntelligencedl
# Clone the repo
git clone https://github.com/walker-hyf/MnTTS.git
cd $PROJECT_ROOT_DIR
βοΈGithub: https://github.com/walker-hyf/mntts
πPaper: https://arxiv.org/abs/2209.10848v1
πDataset: https://paperswithcode.com/dataset/ljspeech
@ArtificialIntelligencedl
π6β€1π₯1
πΈ 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.
βοΈGithub: https://github.com/newbeeer/poisson_flow
πPaper: https://arxiv.org/abs/2209.11178v1
πDataset: 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.
βοΈGithub: https://github.com/newbeeer/poisson_flow
πPaper: https://arxiv.org/abs/2209.11178v1
πDataset: https://paperswithcode.com/dataset/lsun
@ArtificialIntelligencedl
π7β€1π₯1
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π 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
βοΈGithub: https://github.com/liuruoze/mini-AlphaStar
πPaper: https://arxiv.org/abs/2209.11553v1
πHierNet-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
βοΈGithub: https://github.com/liuruoze/mini-AlphaStar
πPaper: https://arxiv.org/abs/2209.11553v1
πHierNet-SC2: https://github.com/liuruoze/hiernet-sc2
@ArtificialIntelligencedl
π7β€1π₯1
π¦Ύ 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.
βοΈGithub: https://github.com/alibaba/easyrec
πPaper: https://arxiv.org/abs/2209.12766v1
πDataset: 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.
βοΈGithub: https://github.com/alibaba/easyrec
πPaper: https://arxiv.org/abs/2209.12766v1
πDataset: https://paperswithcode.com/dataset/criteo
@ArtificialIntelligencedl
π6β€1π₯1π1
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.
βοΈGithub: https://github.com/tagoyal/factuality-datasets
πPaper: https://arxiv.org/abs/2209.12356v1
πDataset: 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.
βοΈGithub: https://github.com/tagoyal/factuality-datasets
πPaper: https://arxiv.org/abs/2209.12356v1
πDataset: https://paperswithcode.com/dataset/cnn-daily-mail-1
@ArtificialIntelligencedl
π3β€1π₯1
π¦Ύ 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.
βοΈGithub: https://github.com/casia-iva-lab/obj2seq
πPaper: https://arxiv.org/abs/2209.13948
πDataset: 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.
βοΈGithub: https://github.com/casia-iva-lab/obj2seq
πPaper: https://arxiv.org/abs/2209.13948
πDataset: https://paperswithcode.com/dataset/coco
@ArtificialIntelligencedl
π4β€1π₯1
π° 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.
βοΈGithub: https://github.com/nicozwy/cofced
πPaper: https://arxiv.org/abs/2209.14642v1
πDataset: 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.
βοΈGithub: https://github.com/nicozwy/cofced
πPaper: https://arxiv.org/abs/2209.14642v1
πDataset: https://paperswithcode.com/dataset/fever
@ArtificialIntelligencedl
π8β€1π₯1