Artificial Intelligence
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Artificial Intelligence

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πŸ“ Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence

git clone https://github.com/SunghwanHong/CATs
cd CATs

conda create -n CATs python=3.6
conda activate CATs

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -U scikit-image
pip install git+https://github.com/albumentations-team/albumentations
pip install tensorboardX termcolor timm tqdm requests pandas


βš™οΈGithub: https://github.com/SunghwanHong/Cost-Aggregation-transformers

πŸ“„Paper: https://arxiv.org/abs/2210.02689v1

πŸ—’Dataset: https://paperswithcode.com/dataset/nerf

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πŸ–₯ CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning

A novel method, CANIFE, that uses canaries - carefully crafted samples by a strong adversary to evaluate the empirical privacy of a training round.

conda create -n "canife" python=3.9
conda activate canife
pip install -r ./requirements.txt


βš™οΈGithub: https://github.com/facebookresearch/canife

πŸ“„Paper: https://arxiv.org/abs/2210.02912v1

πŸ—’Dataset: https://paperswithcode.com/dataset/cifar-10

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πŸ’» A Closer Look at Hardware-Friendly Weight Quantization

βš™οΈGithub: https://github.com/google/qkeras

πŸ“„Paper: https://arxiv.org/abs/2210.03671v1

πŸ—’Training: https://github.com/BertMoons/QuantizedNeuralNetworks-Keras-Tensorflow

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πŸ‘£ OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds

βš™οΈGithub: https://github.com/vlar-group/ogc

πŸ“„Paper: https://arxiv.org/abs/2210.04458v1

β†ͺ️ Demo: https://www.youtube.com/watch?v=dZBjvKWJ4K0

πŸ—’Dataset: https://paperswithcode.com/dataset/kitti

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πŸ›  Understanding Embodied Reference with Touch-Line Transformer

conda create --name nvvc python=3.8
conda activate nvvc
pip install -r requirements.txt
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch


βš™οΈGithub: https://github.com/yang-li-2000/understanding-embodied-reference-with-touch-line-transformer

πŸ“„Paper: https://arxiv.org/abs/2210.05668v2

πŸ—’Dataset: https://paperswithcode.com/dataset/refcoco

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πŸ–₯ Exploring Long-Sequence Masked Autoencoders

βš™οΈGithub: https://github.com/facebookresearch/long_seq_mae

πŸ“„Paper: https://arxiv.org/abs/2210.07224v1

πŸ—’Dataset: https://paperswithcode.com/dataset/places

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πŸ—„ SUPERB-prosody: On The Utility of Self-supervised Models for Prosody-related Tasks

βš™οΈGithub: https://github.com/jsalt-2022-ssl/superb-prosody

πŸ“„Paper: https://arxiv.org/abs/2210.07185v1

πŸ—’Tasks: https://paperswithcode.com/task/prosody-prediction

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πŸ”© SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller

βš™οΈGithub: https://github.com/hkust-knowcomp/subeventwriter

πŸ“„Paper: https://arxiv.org/abs/2210.06694v1

πŸ—’Dataset: https://paperswithcode.com/dataset/wikihow

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πŸ–₯ RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

βš™οΈGithub: https://github.com/m3dv/ribseg

πŸ“„Paper: https://arxiv.org/abs/2210.09309v1

πŸ—’Dataset: https://doi.org/10.5281/zenodo.5336592

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πŸ–₯ HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks (NeurIPS 2022)

πŸ–₯ Github: https://github.com/macderru/hyperdomainnet

πŸ“„Paper: https://arxiv.org/abs/2210.08884v2

πŸ”© Colab: https://colab.research.google.com/drive/1QMylWjzPxvHtxm74U4lWRQXwquw5AaFL#scrollTo=si2tLKYLT-kV

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β†ͺ️ Targeted Adversarial Self-Supervised Learning

πŸ–₯ Github: https://github.com/Kim-Minseon/RoCL

πŸ“„Paper: https://arxiv.org/abs/2210.10482v1

πŸ”© Adversarial Self-Supervised Contrastive Learning: https://sites.google.com/view/rocl2020

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⏩ Learning to Discover and Detect Objects

πŸ–₯ Github: https://github.com/vlfom/rncdl

➑️ Project: https://vlfom.github.io/RNCDL/

πŸ“„Paper: https://arxiv.org/abs/2210.10774v1

πŸ”© Dataset: https://paperswithcode.com/dataset/visual-genome

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⭐️ Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions

πŸ–₯ Github: https://github.com/sinzlab/cgnf

➑️ Model: https://github.com/sinzlab/propose/tree/0.2.0/propose/models/flows

πŸ“„Paper: https://arxiv.org/abs/2210.11179v1

πŸ”© Dataset: https://paperswithcode.com/dataset/human3-6m

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πŸ–₯ TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation

πŸ–₯ Github: https://github.com/air-discover/toist

πŸ“„Paper: https://arxiv.org/abs/2210.10775v1

πŸ”© Dataset: https://github.com/coco-tasks/dataset

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Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

πŸ–₯ Github: https://github.com/mv-lab/AISP

πŸ“„Paper: https://arxiv.org/abs/2210.11153v1

πŸ”© Starter guide: https://github.com/mv-lab/AISP/blob/main/aim22-reverseisp/official-starter-code.ipynb

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πŸ–₯ NVIDIA Federated Learning Application Runtime Environment

NVIDIA FLARE enables researchers to collaborate and build AI models without sharing private data.

pip install nvflare

πŸ–₯ Github: https://github.com/nvidia/nvflare

πŸ“„Paper: https://arxiv.org/abs/2210.13291v1

πŸ”© Starter guide: https://nvflare.readthedocs.io/en/main/getting_started.html

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πŸ–₯ Evaluating Long-Term Memory in 3D Mazes

pip install memory-maze

πŸ–₯ Github: https://github.com/jurgisp/memory-maze

πŸ“„Paper: https://arxiv.org/abs/2210.13383v1

πŸ”© Starter guide: https://www.dropbox.com/sh/c38sc5h7ltgyyzc/AAARVeKgnyaoBLGdYYVABh_Ja

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