Masked Autoencoders that Listen
Github: https://github.com/facebookresearch/audiomae
Paper: https://arxiv.org/abs/2207.06405v1
Dataset : https://paperswithcode.com/dataset/audioset
@ai_machinelearning_big_data
Github: https://github.com/facebookresearch/audiomae
Paper: https://arxiv.org/abs/2207.06405v1
Dataset : https://paperswithcode.com/dataset/audioset
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GitHub
GitHub - facebookresearch/AudioMAE: This repo hosts the code and models of "Masked Autoencoders that Listen".
This repo hosts the code and models of "Masked Autoencoders that Listen". - facebookresearch/AudioMAE
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🧍♂ PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision
Human-centric privacy-preserving synthetic data generator with highly parametrized domain randomization.
Github: https://github.com/unity-technologies/peoplesanspeople
Paper: https://arxiv.org/abs/2207.05025v1
Demo Video: https://www.youtube.com/watch?v=mQ_DUdB70dc
@ai_machinelearning_big_data
Human-centric privacy-preserving synthetic data generator with highly parametrized domain randomization.
Github: https://github.com/unity-technologies/peoplesanspeople
Paper: https://arxiv.org/abs/2207.05025v1
Demo Video: https://www.youtube.com/watch?v=mQ_DUdB70dc
@ai_machinelearning_big_data
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The AIoT Revolution: How AI and IoT Are Transforming Our World
https://www.kdnuggets.com/2022/07/aiot-revolution-ai-iot-transforming-world.html
@ai_machinelearning_big_data
https://www.kdnuggets.com/2022/07/aiot-revolution-ai-iot-transforming-world.html
@ai_machinelearning_big_data
🏎 Instance Shadow Detection with A Single-Stage Detector
Deep framework, and an evaluation metric to approach this new task.
Github: https://github.com/stevewongv/InstanceShadowDetection
Instance Shadow Detection: https://github.com/stevewongv/SSIS
Video: https://www.youtube.com/watch?v=p0b_2SsFypw
Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing
Paper: https://arxiv.org/abs/2207.04614v1
Datasets: https://paperswithcode.com/dataset/soba
@ai_machinelearning_big_data
Deep framework, and an evaluation metric to approach this new task.
Github: https://github.com/stevewongv/InstanceShadowDetection
Instance Shadow Detection: https://github.com/stevewongv/SSIS
Video: https://www.youtube.com/watch?v=p0b_2SsFypw
Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing
Paper: https://arxiv.org/abs/2207.04614v1
Datasets: https://paperswithcode.com/dataset/soba
@ai_machinelearning_big_data
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Class-incremental Novel Class Discovery
Github: https://github.com/oatmealliu/class-incd
Paper: https://arxiv.org/abs/2207.08605v1
Dataset: https://paperswithcode.com/dataset/tiny-imagenet
@ai_machinelearning_big_data
Github: https://github.com/oatmealliu/class-incd
Paper: https://arxiv.org/abs/2207.08605v1
Dataset: https://paperswithcode.com/dataset/tiny-imagenet
@ai_machinelearning_big_data
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KGI (Knowledge Graph Induction) for slot filling
Github: https://github.com/ibm/kgi-slot-filling
KILT data and knowledge source: https://github.com/facebookresearch/KILT
Paper: https://arxiv.org/abs/2207.06300v1
Dataset: https://paperswithcode.com/dataset/natural-questions
@ai_machinelearning_big_data
Github: https://github.com/ibm/kgi-slot-filling
KILT data and knowledge source: https://github.com/facebookresearch/KILT
Paper: https://arxiv.org/abs/2207.06300v1
Dataset: https://paperswithcode.com/dataset/natural-questions
@ai_machinelearning_big_data
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🎯 Object-Compositional Neural Implicit Surfaces
Github: https://github.com/qianyiwu/objsdf
Paper: https://arxiv.org/abs/2207.09686v1
Project: https://qianyiwu.github.io/objectsdf/
Dataset: https://paperswithcode.com/dataset/scannet
Github: https://github.com/qianyiwu/objsdf
Paper: https://arxiv.org/abs/2207.09686v1
Project: https://qianyiwu.github.io/objectsdf/
Dataset: https://paperswithcode.com/dataset/scannet
GitHub
GitHub - QianyiWu/objsdf: :t-rex: [ECCV‘22] Pytorch implementation of 'Object-Compositional Neural Implicit Surfaces'
:t-rex: [ECCV‘22] Pytorch implementation of 'Object-Compositional Neural Implicit Surfaces' - QianyiWu/objsdf
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Benchmarking Omni-Vision Representation through the Lens of Visual Realms
Github: https://github.com/ZhangYuanhan-AI/OmniBenchmark
Project: https://zhangyuanhan-ai.github.io/OmniBenchmark
Paper: https://arxiv.org/abs/2207.07106v1
Competition: https://codalab.lisn.upsaclay.fr/competitions/6043
@ai_machinelearning_big_data
Github: https://github.com/ZhangYuanhan-AI/OmniBenchmark
Project: https://zhangyuanhan-ai.github.io/OmniBenchmark
Paper: https://arxiv.org/abs/2207.07106v1
Competition: https://codalab.lisn.upsaclay.fr/competitions/6043
@ai_machinelearning_big_data
👍10
Distance Learner: Incorporating Manifold Prior to Model Training
Github: https://github.com/microsoft/distance-learner
Paper: https://arxiv.org/abs/2207.06888v1
Project: https://fast-vid2vid.github.io/
@ai_machinelearning_big_data
Github: https://github.com/microsoft/distance-learner
Paper: https://arxiv.org/abs/2207.06888v1
Project: https://fast-vid2vid.github.io/
@ai_machinelearning_big_data
GitHub
GitHub - microsoft/distance-learner: Official implementation for "Distance Learner: Incorporating Manifold Prior to Model Training"
Official implementation for "Distance Learner: Incorporating Manifold Prior to Model Training" - microsoft/distance-learner
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🚀 Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting
Github: https://github.com/hikopensource/davar-lab-ocr
Paper: https://arxiv.org/abs/2207.06694v1
Dataset: https://paperswithcode.com/dataset/total-text
@ai_machinelearning_big_data
Github: https://github.com/hikopensource/davar-lab-ocr
Paper: https://arxiv.org/abs/2207.06694v1
Dataset: https://paperswithcode.com/dataset/total-text
@ai_machinelearning_big_data
👍8❤4
Language Modelling with Pixels
PIXEL is a language model that operates on text rendered as images, fully removing the need for a fixed vocabulary.
Github: https://github.com/xplip/pixel
Paper: https://arxiv.org/abs/2207.06991v1
Dataset: https://paperswithcode.com/dataset/glue
Pretrained: https://huggingface.co/Team-PIXEL/pixel-base
@ai_machinelearning_big_data
PIXEL is a language model that operates on text rendered as images, fully removing the need for a fixed vocabulary.
Github: https://github.com/xplip/pixel
Paper: https://arxiv.org/abs/2207.06991v1
Dataset: https://paperswithcode.com/dataset/glue
Pretrained: https://huggingface.co/Team-PIXEL/pixel-base
@ai_machinelearning_big_data
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⚡️ CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS
Github: https://github.com/walkerning/aw_nas
Paper: https://arxiv.org/abs/2207.07868v1
Dataset: https://paperswithcode.com/dataset/nas-bench-201
@ai_machinelearning_big_data
Github: https://github.com/walkerning/aw_nas
Paper: https://arxiv.org/abs/2207.07868v1
Dataset: https://paperswithcode.com/dataset/nas-bench-201
@ai_machinelearning_big_data
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HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Github: https://github.com/amirhossein-kz/hiformer
Paper: https://arxiv.org/abs/2207.08518v1
Tasks: https://paperswithcode.com/task/medical-image-segmentation
@ai_machinelearning_big_data
Github: https://github.com/amirhossein-kz/hiformer
Paper: https://arxiv.org/abs/2207.08518v1
Tasks: https://paperswithcode.com/task/medical-image-segmentation
@ai_machinelearning_big_data
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📝 Automated Crossword Solving
Pretrained models, precomputed FAISS embeddings, and a crossword clue-answer dataset.
Github: https://github.com/albertkx/berkeley-crossword-solver
Paper: https://arxiv.org/abs/2205.09665v1
Dataset: https://www.xwordinfo.com/JSON/
Pretrained models, precomputed FAISS embeddings, and a crossword clue-answer dataset.
Github: https://github.com/albertkx/berkeley-crossword-solver
Paper: https://arxiv.org/abs/2205.09665v1
Dataset: https://www.xwordinfo.com/JSON/
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🔧 FSD: Fully Sparse 3D Object Detection & SST: Single-stride Sparse Transformer
Github: https://github.com/tusimple/sst
Paper: http://arxiv.org/abs/2207.10035
Dataset: https://paperswithcode.com/dataset/waymo-open-dataset
@ai_machinelearning_big_data
Github: https://github.com/tusimple/sst
Paper: http://arxiv.org/abs/2207.10035
Dataset: https://paperswithcode.com/dataset/waymo-open-dataset
@ai_machinelearning_big_data
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✨ Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification
Tip-Adapter is a training-free adaption method for CLIP to conduct few-shot classification.
Github: https://github.com/gaopengcuhk/tip-adapter
Paper: https://arxiv.org/abs/2207.09519v1
Dataset: https://paperswithcode.com/dataset/oxford-102-flower
@ai_machinelearning_big_data
Tip-Adapter is a training-free adaption method for CLIP to conduct few-shot classification.
Github: https://github.com/gaopengcuhk/tip-adapter
Paper: https://arxiv.org/abs/2207.09519v1
Dataset: https://paperswithcode.com/dataset/oxford-102-flower
@ai_machinelearning_big_data
👍10❤1
Using Scikit-learn’s Imputer
https://www.kdnuggets.com/2022/07/scikitlearn-imputer.html
@ai_machinelearning_big_data
https://www.kdnuggets.com/2022/07/scikitlearn-imputer.html
@ai_machinelearning_big_data
👍10👎1
🧿 Generative Multiplane Images: Making a 2D GAN 3D-Aware
What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator.
Github: https://github.com/apple/ml-gmpi
Paper: https://arxiv.org/abs/2207.10642v1
Dataset: https://paperswithcode.com/dataset/metfaces
Project: https://xiaoming-zhao.github.io/projects/gmpi/
Pretrained checkpoints: https://drive.google.com/drive/folders/1MEIjen0XOIW-kxEMfBUONnKYrkRATSR_
@ai_machinelearning_big_data
What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator.
Github: https://github.com/apple/ml-gmpi
Paper: https://arxiv.org/abs/2207.10642v1
Dataset: https://paperswithcode.com/dataset/metfaces
Project: https://xiaoming-zhao.github.io/projects/gmpi/
Pretrained checkpoints: https://drive.google.com/drive/folders/1MEIjen0XOIW-kxEMfBUONnKYrkRATSR_
@ai_machinelearning_big_data
👍14
Machine Learning Algorithms Explained in Less Than 1 Minute Each
https://www.kdnuggets.com/2022/07/machine-learning-algorithms-explained-less-1-minute.html
@ai_machinelearning_big_data
https://www.kdnuggets.com/2022/07/machine-learning-algorithms-explained-less-1-minute.html
@ai_machinelearning_big_data
👍14🔥1
🏵 Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation.
Github: https://github.com/164140757/scm
Paper: https://arxiv.org/abs/2207.10447v1
Dataset: https://paperswithcode.com/dataset/cub-200-2011
@ai_machinelearning_big_data
learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation.
Github: https://github.com/164140757/scm
Paper: https://arxiv.org/abs/2207.10447v1
Dataset: https://paperswithcode.com/dataset/cub-200-2011
@ai_machinelearning_big_data
👍9
🌟 SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
Simple but very effective attention module for Convolutional Neural Networks (ConvNets).
Github: https://github.com/ZjjConan/SimAM
Paper: http://proceedings.mlr.press/v139/yang21o.html
Dataset: https://paperswithcode.com/dataset/cifar-10
Google Drive: https://drive.google.com/drive/folders/1rRT0UCPeRLPdTCJvv43hvAnGnS49nIWn?usp=sharing
@ai_machinelearning_big_data
Simple but very effective attention module for Convolutional Neural Networks (ConvNets).
Github: https://github.com/ZjjConan/SimAM
Paper: http://proceedings.mlr.press/v139/yang21o.html
Dataset: https://paperswithcode.com/dataset/cifar-10
Google Drive: https://drive.google.com/drive/folders/1rRT0UCPeRLPdTCJvv43hvAnGnS49nIWn?usp=sharing
@ai_machinelearning_big_data
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