π LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection
Waymo Open Dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology.
Github: https://github.com/waymo-research/waymo-open-dataset
Paper: https://arxiv.org/abs/2206.07705v1
Dataset: https://paperswithcode.com/dataset/waymo-open-datasetg
@ArtificialIntelligencedl
Waymo Open Dataset publicly to aid the research community in making advancements in machine perception and autonomous driving technology.
Github: https://github.com/waymo-research/waymo-open-dataset
Paper: https://arxiv.org/abs/2206.07705v1
Dataset: https://paperswithcode.com/dataset/waymo-open-datasetg
@ArtificialIntelligencedl
π2
Forwarded from Machinelearning
π StrengthNet
Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"
Github: https://github.com/ttslr/strengthnet
Paper: https://arxiv.org/abs/2110.03156
MOSNet: https://github.com/lochenchou/MOSNet
@ai_machinelearning_big_data
Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"
Github: https://github.com/ttslr/strengthnet
Paper: https://arxiv.org/abs/2110.03156
MOSNet: https://github.com/lochenchou/MOSNet
@ai_machinelearning_big_data
π5
DeepFormableTag: End-to-end Generation and Recognition of Deformable Fiducial Markers
Github: https://github.com/KAIST-VCLAB/DeepFormableTag
Project: https://vclab.kaist.ac.kr/siggraph2021/index.html
Paper: https://vclab.kaist.ac.kr/siggraph2021/DeepFormableTag-main-screen.pdf
Dataset: https://drive.google.com/drive/folders/1picphIb6Hbj6pM3Wu_Vxu53wzKBV0jdV
@ArtificialIntelligencedl
Github: https://github.com/KAIST-VCLAB/DeepFormableTag
Project: https://vclab.kaist.ac.kr/siggraph2021/index.html
Paper: https://vclab.kaist.ac.kr/siggraph2021/DeepFormableTag-main-screen.pdf
Dataset: https://drive.google.com/drive/folders/1picphIb6Hbj6pM3Wu_Vxu53wzKBV0jdV
@ArtificialIntelligencedl
π5
π Spatially-Adapive Multilayer (SAM) Inversion
Proposed method automatically selects the latent space tailored for each region to balance the reconstruction quality and editability (3rd row).
Github: https://github.com/adobe-research/sam_inversion
Project: https://www.cs.cmu.edu/~SAMInversion/
Paper: https://arxiv.org/abs/2206.08357
@ArtificialIntelligencedl
Proposed method automatically selects the latent space tailored for each region to balance the reconstruction quality and editability (3rd row).
Github: https://github.com/adobe-research/sam_inversion
Project: https://www.cs.cmu.edu/~SAMInversion/
Paper: https://arxiv.org/abs/2206.08357
@ArtificialIntelligencedl
π5
Automatic Prosody Annotation with Pre-Trained Text-Speech Model
Github: https://github.com/daisyqk/automatic-prosody-annotation
Project: https://daisyqk.github.io/Automatic-Prosody-Annotation_w/
Paper: https://arxiv.org/abs/2206.07956v1
@ArtificialIntelligencedl
Github: https://github.com/daisyqk/automatic-prosody-annotation
Project: https://daisyqk.github.io/Automatic-Prosody-Annotation_w/
Paper: https://arxiv.org/abs/2206.07956v1
@ArtificialIntelligencedl
π₯6π2
π¦Ύ Bi-DexHands: Bimanual Dexterous Manipulation via Reinforcement Learning
Bi-DexHands provides a collection of bimanual dexterous manipulations tasks and reinforcement learning algorithms.
Github: https://github.com/pku-marl/dexteroushands
Isaac Gym: https://developer.nvidia.com/isaac-gym
Paper: hhttps://arxiv.org/abs/2206.08686
@ArtificialIntelligencedl
Bi-DexHands provides a collection of bimanual dexterous manipulations tasks and reinforcement learning algorithms.
Github: https://github.com/pku-marl/dexteroushands
Isaac Gym: https://developer.nvidia.com/isaac-gym
Paper: hhttps://arxiv.org/abs/2206.08686
@ArtificialIntelligencedl
π6
πΉ SENSORIUM 2022 Competition
The Sensorium competition on predicting large-scale mouse primary visual cortex activity
Github: https://github.com/sinzlab/sensorium
Website: https://sensorium2022.net/
Paper: https://arxiv.org/abs/2206.08666v1
@ArtificialIntelligencedl
The Sensorium competition on predicting large-scale mouse primary visual cortex activity
Github: https://github.com/sinzlab/sensorium
Website: https://sensorium2022.net/
Paper: https://arxiv.org/abs/2206.08666v1
@ArtificialIntelligencedl
π5
π Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping
Github:https://github.com/airlab-polimi/c-slam
Tutorial: https://ros.org/wiki/catkin/Tutorials/create_a_workspace
Paper: https://arxiv.org/abs/2206.10263v1
@ArtificialIntelligencedl
Github:https://github.com/airlab-polimi/c-slam
Tutorial: https://ros.org/wiki/catkin/Tutorials/create_a_workspace
Paper: https://arxiv.org/abs/2206.10263v1
@ArtificialIntelligencedl
π5
π§ Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions
Github: https://github.com/Deep-MI/FastSurfer
Colab: https://colab.research.google.com/github/Deep-MI/FastSurfer/blob/master/Tutorial/Tutorial_FastSurferCNN_QuickSeg.ipynb
Paper: https://arxiv.org/abs/2206.14919v1
Github: https://github.com/Deep-MI/FastSurfer
Colab: https://colab.research.google.com/github/Deep-MI/FastSurfer/blob/master/Tutorial/Tutorial_FastSurferCNN_QuickSeg.ipynb
Paper: https://arxiv.org/abs/2206.14919v1
π© Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping
PyGOD is a Python library for graph outlier detection (anomaly detection).
Github: https://github.com/pygod-team/pygod
Dataset : https://paperswithcode.com/dataset/ogb
Paper: https://arxiv.org/abs/2206.10071v1
@ArtificialIntelligencedl
PyGOD is a Python library for graph outlier detection (anomaly detection).
Github: https://github.com/pygod-team/pygod
Dataset : https://paperswithcode.com/dataset/ogb
Paper: https://arxiv.org/abs/2206.10071v1
@ArtificialIntelligencedl
π7
π» DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
DaisyRec-v2.0 is a Python toolkit developed for benchmarking top-N recommendation task.
Github: https://github.com/recsys-benchmark/daisyrec-v2.0
Command Generator : https://daisyrecguicommandgenerator.pythonanywhere.com/
Paper: https://arxiv.org/abs/2206.10848v1
Tutorial: https://github.com/recsys-benchmark/DaisyRec-v2.0/blob/main/DaisyRec-v2.0-Tutorial.ipynb
@ArtificialIntelligencedl
DaisyRec-v2.0 is a Python toolkit developed for benchmarking top-N recommendation task.
Github: https://github.com/recsys-benchmark/daisyrec-v2.0
Command Generator : https://daisyrecguicommandgenerator.pythonanywhere.com/
Paper: https://arxiv.org/abs/2206.10848v1
Tutorial: https://github.com/recsys-benchmark/DaisyRec-v2.0/blob/main/DaisyRec-v2.0-Tutorial.ipynb
@ArtificialIntelligencedl
π7
Frequency Dynamic Convolution-Recurrent Neural Network (FDY-CRNN) for Sound Event Detection
Frequency Dynamic Convolution applied kernel that adapts to each freqeuncy bin of input, in order to remove tranlation equivariance of 2D convolution along the frequency axis.
Github: https://github.com/frednam93/FDY-SED
Paper: https://arxiv.org/abs/2206.11645v1
Dataset: https://paperswithcode.com/dataset/desed
@ArtificialIntelligencedl
Frequency Dynamic Convolution applied kernel that adapts to each freqeuncy bin of input, in order to remove tranlation equivariance of 2D convolution along the frequency axis.
Github: https://github.com/frednam93/FDY-SED
Paper: https://arxiv.org/abs/2206.11645v1
Dataset: https://paperswithcode.com/dataset/desed
@ArtificialIntelligencedl
π₯2
π
Retrosynthetic Planning with Retro*
graph-based search policy that eliminates the redundant explorations of any intermediate molecules.
Github: https://github.com/binghong-ml/retro_star
Paper: https://arxiv.org/abs/2206.11477v1
Dataset: https://www.dropbox.com/s/ar9cupb18hv96gj/retro_data.zip?dl=0
@ArtificialIntelligencedl
graph-based search policy that eliminates the redundant explorations of any intermediate molecules.
Github: https://github.com/binghong-ml/retro_star
Paper: https://arxiv.org/abs/2206.11477v1
Dataset: https://www.dropbox.com/s/ar9cupb18hv96gj/retro_data.zip?dl=0
@ArtificialIntelligencedl
π₯4
DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models
Github: https://github.com/wgcban/ddpm-cd
Project: https://www.wgcban.com/research#h.ar24vwqlm021
Paper: https://arxiv.org/abs/2206.11892
Dataset: https://paperswithcode.com/dataset/fmow
@ArtificialIntelligencedl
Github: https://github.com/wgcban/ddpm-cd
Project: https://www.wgcban.com/research#h.ar24vwqlm021
Paper: https://arxiv.org/abs/2206.11892
Dataset: https://paperswithcode.com/dataset/fmow
@ArtificialIntelligencedl
π2
SETR - Pytorch
Github: https://github.com/920232796/setr-pytorch
Paper: https://arxiv.org/abs/2206.11520v1
Dataset: https://www.kaggle.com/c/carvana-image-masking-challenge/data
@ArtificialIntelligencedl
Github: https://github.com/920232796/setr-pytorch
Paper: https://arxiv.org/abs/2206.11520v1
Dataset: https://www.kaggle.com/c/carvana-image-masking-challenge/data
@ArtificialIntelligencedl
π3
Complementary datasets to COCO for object detection
Github: https://github.com/aliborji/coco_oi
Paper: https://arxiv.org/abs/2206.11473v1
Dataset: https://paperswithcode.com/dataset/coco
@ArtificialIntelligencedl
Github: https://github.com/aliborji/coco_oi
Paper: https://arxiv.org/abs/2206.11473v1
Dataset: https://paperswithcode.com/dataset/coco
@ArtificialIntelligencedl
π₯°1
β‘οΈ K-CAI NEURAL API
KCAI NEURAL API Keras based neural network API that will allow you to prototype
Github: https://github.com/joaopauloschuler/k-neural-api
Colab: https://colab.research.google.com/github/joaopauloschuler/k-neural-api/blob/master/examples/jupyter/simple_image_classification_with_any_dataset.ipynb
Paper: https://www.researchgate.net/publication/360226228_Grouped_Pointwise_Convolutions_Reduce_Parameters_in_Convolutional_Neural_Networks
Dataset: https://paperswithcode.com/dataset/plantdoc
@ArtificialIntelligencedl
KCAI NEURAL API Keras based neural network API that will allow you to prototype
Github: https://github.com/joaopauloschuler/k-neural-api
Colab: https://colab.research.google.com/github/joaopauloschuler/k-neural-api/blob/master/examples/jupyter/simple_image_classification_with_any_dataset.ipynb
Paper: https://www.researchgate.net/publication/360226228_Grouped_Pointwise_Convolutions_Reduce_Parameters_in_Convolutional_Neural_Networks
Dataset: https://paperswithcode.com/dataset/plantdoc
@ArtificialIntelligencedl
π₯4
π‘ Denoised MDPs: Learning World Models Better Than the World Itself
Github: https://github.com/facebookresearch/denoised_mdp
Project: https://ssnl.github.io/denoised_mdp
Paper: https://arxiv.org/abs/2206.15477v1
Dataset: https://paperswithcode.com/dataset/deepmind-control-suite
@ArtificialIntelligencedl
Github: https://github.com/facebookresearch/denoised_mdp
Project: https://ssnl.github.io/denoised_mdp
Paper: https://arxiv.org/abs/2206.15477v1
Dataset: https://paperswithcode.com/dataset/deepmind-control-suite
@ArtificialIntelligencedl
π4β€1
π² Forecasting Future World Events with Neural Networks
Github: https://github.com/andyzoujm/autocast
Paper: https://arxiv.org/abs/2206.15474v1
Dataset: https://people.eecs.berkeley.edu/~hendrycks/intervalqa.tar.gz
@ArtificialIntelligencedl
Github: https://github.com/andyzoujm/autocast
Paper: https://arxiv.org/abs/2206.15474v1
Dataset: https://people.eecs.berkeley.edu/~hendrycks/intervalqa.tar.gz
@ArtificialIntelligencedl
π4
BigBIO: Biomedical Datasets
Currently BigBIO provides support for
more than 120 biomedical datasets
10 languages
Harmonized dataset schemas by task type
Metadata on licensing, coarse/fine-grained task types, domain, and more!
Github: https://github.com/bigscience-workshop/biomedical
Paper: https://arxiv.org/abs/2206.15076v1
Dataset: https://paperswithcode.com/dataset/bioasq
@ArtificialIntelligencedl
Currently BigBIO provides support for
more than 120 biomedical datasets
10 languages
Harmonized dataset schemas by task type
Metadata on licensing, coarse/fine-grained task types, domain, and more!
Github: https://github.com/bigscience-workshop/biomedical
Paper: https://arxiv.org/abs/2206.15076v1
Dataset: https://paperswithcode.com/dataset/bioasq
@ArtificialIntelligencedl
π2π₯1
π MMFN: Multi-Modal Fusion Net for End-to-End Autonomous Driving
A novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks.
Github: https://github.com/Kin-Zhang/mmfn
Paper: https://arxiv.org/abs/2207.00186v1
Dataset: https://github.com/carla-simulator/leaderboard/issues/81
@ArtificialIntelligencedl
A novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks.
Github: https://github.com/Kin-Zhang/mmfn
Paper: https://arxiv.org/abs/2207.00186v1
Dataset: https://github.com/carla-simulator/leaderboard/issues/81
@ArtificialIntelligencedl
π4