🔹 TensoRF: Tensorial Radiance Fields

A novel approach to model and reconstruct radiance fields

Github: https://github.com/apchenstu/TensoRF

Paper: https://arxiv.org/abs/2203.09517v1

Dataset: https://paperswithcode.com/dataset/ffhq

Project page: https://apchenstu.github.io/TensoRF/

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💬 Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation


Github: https://github.com/mgaido91/FBK-fairseq-ST

Paper: https://arxiv.org/abs/2203.09866v1

Dataset: https://paperswithcode.com/dataset/winobias

Project page: https://apchenstu.github.io/TensoRF/

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🔎 Полное собрание шпаргалок по машинному обучению, статистике, Python и R

Article

@ArtificialIntelligencedl

🪄 Generative Invariance Transfer

Github: https://github.com/allanyangzhou/generative-invariance-transfer

Paper: https://arxiv.org/abs/2203.09739v1

Dataset: https://paperswithcode.com/dataset/cifar-10

@ArtificialIntelligencedl

🚀 AOT (Associating Objects with Transformers for Video Object Segmentation) in PyTorch

Github: https://github.com/yoxu515/aot-benchmark

Paper: https://arxiv.org/pdf/2203.11442v1.pdf

Dataset: https://paperswithcode.com/dataset/davis

Demo: https://github.com/yoxu515/aot-benchmark/blob/main/datasets/Demo

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📑 PubTables-1M: Towards comprehensive table extraction from unstructured documents

Github: https://github.com/microsoft/table-transformer

Paper: https://arxiv.org/pdf/2110.00061.pdf

Dataset: https://msropendata.com/datasets/505fcbe3-1383-42b1-913a-f651b8b712d3

https://t.iss.one/ArtificialIntelligencedl

🦾 Global Tracking Transformers

Github: https://github.com/xingyizhou/GTR

Demo: https://github.com/facebookresearch/detectron2/blob/main/GETTING_STARTED.md

Paper: https://arxiv.org/abs/2203.13250v1

Dataset: https://paperswithcode.com/dataset/mot17

https://t.iss.one/ArtificialIntelligencedl

⚡️ EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Github: https://github.com/tjiiv-cprg/epro-pnp

Paper: https://arxiv.org/abs/2203.13254v1

Dataset: https://paperswithcode.com/dataset/nuscenes

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🎨 Autoregressive Image Generation using Residual Quantization

Github: https://github.com/kakaobrain/rq-vae-transformer

Pytorch: https://github.com/lucidrains/vector-quantize-pytorch

Paper: https://arxiv.org/abs/2203.01941v2

Dataset: https://paperswithcode.com/dataset/lsun

@ArtificialIntelligencedl

◾️ pyABC: Efficient and robust easy-to-use approximate Bayesian computation

Github: https://github.com/icb-dcm/pyabc

Paper: https://arxiv.org/abs/2203.13043v1

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🧍 Discovering Human-Object Interaction Concepts via Self-Compositional Learning

Github: https://github.com/zhihou7/HOI-CL

Paper: https://arxiv.org/abs/2203.14272v1

Dataset: https://paperswithcode.com/dataset/hico-det

@ArtificialIntelligencedl

Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching

A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net)

Github: https://github.com/dongkwonjin/Semantic-Line-DRM

Code: https://github.com/dongkwonjin/Semantic-Line-SLNet

Paper: https://arxiv.org/abs/2203.15285v1

Dataset: https://paperswithcode.com/dataset/sel

@ArtificialIntelligencedl

Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching

A novel approach where the two processes for activity classification and entity estimation are interactive and complementary.

Github: https://github.com/jhcho99/coformer

Architecture: https://github.com/jhcho99/gsrtr

Paper: https://arxiv.org/abs/2203.16518v1

Dataset: https://paperswithcode.com/dataset/framenet

@ArtificialIntelligencedl

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💥 T5X is a modular, composable, research-friendly framework for high-performance, configurable, self-service training, evaluation, and inference of sequence models.

Github: https://github.com/google-research/t5x

Paper: https://arxiv.org/abs/2203.17189v1

@ArtificialIntelligencedl

💻 TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing (CVPR 2022)

Recent advances like StyleGAN have promoted the growth of controllable facial editing.

Github: https://github.com/billyxyb/transeditor

Paper: https://arxiv.org/abs/2203.17266v1

@ArtificialIntelligencedl

🔍 Exploiting Explainable Metrics for Augmented SGD

A new explainability metrics that measure the redundant information in a network's layers and exploit this information to augment the Stochastic Gradient Descent

Project

Code: https://github.com/mahdihosseini/rmsgd

Paper: https://arxiv.org/pdf/2203.16723v1.pdf

Dataset: https://paperswithcode.com/dataset/mhist

@ArtificialIntelligencedl

🎆 Efficient Non-Autoregressive GAN Voice Conversion using VQWav2vec Features and Dynamic Convolution

Dynamic-GAN-VC (DYGAN-VC), uses a non-autoregressive structure and makes use of vector quantised embeddings obtained from a VQWav2vec model

Code: https://github.com/mingjiechen/dyganvc

Paper: https://arxiv.org/abs/2203.17172v1

Dataset: https://github.com/nii-yamagishilab/VCC2020-database

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➕ Rethinking Portrait Matting with Privacy Preserving

Code: https://github.com/mingjiechen/dyganvc

Paper: https://arxiv.org/abs/2203.16828v1

Dataset: https://github.com/vitae-transformer/vitae-transformer-matting#ppt-setting-and-p3m-10k-dataset

@ArtificialIntelligencedl

📊 MultiMAE: Multi-modal Multi-task Masked Autoencoders

An efficient and effective pre-training strategy for Vision Transformers

Project: https://multimae.epfl.ch/

Code: https://github.com/EPFL-VILAB/MultiMAE

Paper: https://arxiv.org/abs/2204.01678

Project: https://multimae.epfl.ch/

@ArtificialIntelligencedl

📌 TESTR: Text Spotting Transformers

TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild

Code: https://github.com/mlpc-ucsd/testr

Paper: https://arxiv.org/abs/2204.01918

Dataset: https://ucsdcloud-my.sharepoint.com/:u:/g/personal/xiz102_ucsd_edu/EWgEM5BSRjBEua4B_qLrGR0BaombUL8K3d23ldXOb7wUNA?e=7VzH34

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