Artificial Intelligence – Telegram
Artificial Intelligence
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Artificial Intelligence

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@ai_machinelearning_big_data - Machine learning channel

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Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation

Github: https://github.com/antabangun/coex

Paper:
https://arxiv.org/abs/2108.05773v1

Project: https://antabangun.github.io/projects/CoEx/#demo

@ArtificialIntelligencedl
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation

Github: https://github.com/crockwell/pixelsynth

Paper:
https://arxiv.org/abs/2108.05892v1

@ArtificialIntelligencedl
Towards Efficient and Data Agnostic Image Classification Training Pipeline for Embedded Systems

Github: https://github.com/openvinotoolkit/training_extensions

Paper:
https://arxiv.org/abs/2108.07049v1

@ArtificialIntelligencedl
👎1
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Github: https://github.com/DengPingFan/Polyp-PVT

Paper:
https://arxiv.org/abs/2108.06932v1

@ArtificialIntelligencedl
💡 X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics

Github: https://github.com/yehli/xmodaler

Paper: https://arxiv.org/abs/2108.08217v1

Project: https://xmodaler.readthedocs.io/en/latest/

@ArtificialIntelligencedl
Forwarded from Machinelearning
🎩 Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval

Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages.

Github: https://github.com/castorini/mr.tydi

Paper: https://arxiv.org/abs/2108.08787

Tasks: https://paperswithcode.com/task/representation-learning

@ai_machinelearning_big_data
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ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

Github: https://github.com/hanchaoleng/shapeconv

Paper: https://arxiv.org/abs/2108.10528v1


@ArtificialIntelligencedl
🥰1
Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning

Github: https://github.com/zengarden/momentum2-teacher

Paper: https://arxiv.org/abs/2108.10668v1

@ArtificialIntelligencedl
🚘 YOLOP: You Only Look Once for Panoptic Driving Perception

Github: https://github.com/hustvl/yolop

Paper: https://arxiv.org/abs/2108.11250v2

@ArtificialIntelligencedl
➡️ Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics

Github: https://github.com/vita-group/tegnas

Paper: https://arxiv.org/abs/2108.11939v1

@ArtificialIntelligencedl
Forwarded from Machinelearning
💬 Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Github: https://github.com/ofirpress/attention_with_linear_biases

Paper: https://ofir.io/train_short_test_long.pdf

Fairseq: https://github.com/pytorch/fairseq

@ai_machinelearning_big_data
🌐 A Partition Filter Network for Joint Entity and Relation Extraction

Github: https://github.com/Coopercoppers/PFN

Paper: https://arxiv.org/abs/2108.12202v2

@ArtificialIntelligencedl
Iterative Filter Adaptive Network for Single Image Defocus Deblurring

Github: https://github.com/codeslake/IFAN

Paper: https://arxiv.org/abs/2108.13610v1

@ArtificialIntelligencedl
🌝🌚 PyGCL: Graph Contrastive Learning for PyTorch

Github: https://github.com/GraphCL/PyGCL

Paper: https://arxiv.org/abs/2109.01116v1

@ArtificialIntelligencedl
👁 NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo

Github: https://github.com/weiyithu/nerfingmvs

Paper: https://arxiv.org/abs/2109.01129v1

Project: https://weiyithu.github.io/NerfingMVS

@ArtificialIntelligencedl