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AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars
Github: https://github.com/hongfz16/avatarclip
Colab demo: https://colab.research.google.com/drive/1dfaecX7xF3nP6fyXc8XBljV5QY1lc1TR?usp=sharing
Paper: https://arxiv.org/abs/2205.08535v1
Dataset: https://paperswithcode.com/dataset/amass
Instructions: https://github.com/hongfz16/AvatarCLIP/blob/main/Avatar2FBX/README.md
👉 @bigdata_1
Github: https://github.com/hongfz16/avatarclip
Colab demo: https://colab.research.google.com/drive/1dfaecX7xF3nP6fyXc8XBljV5QY1lc1TR?usp=sharing
Paper: https://arxiv.org/abs/2205.08535v1
Dataset: https://paperswithcode.com/dataset/amass
Instructions: https://github.com/hongfz16/AvatarCLIP/blob/main/Avatar2FBX/README.md
👉 @bigdata_1
❤🔥1👍1🍓1
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PYSKL: Towards Good Practices for Skeleton Action Recognition
Skeleton-based action recognition
Github: https://github.com/kennymckormick/pyskl
Paper: https://arxiv.org/abs/2205.09443v1
Dataset: https://paperswithcode.com/dataset/finegym
👉 @bigdata_1
Skeleton-based action recognition
Github: https://github.com/kennymckormick/pyskl
Paper: https://arxiv.org/abs/2205.09443v1
Dataset: https://paperswithcode.com/dataset/finegym
👉 @bigdata_1
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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/
👉 @bigdata_1
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/
👉 @bigdata_1
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Imagen unprecedented photorealism × deep level of language understanding From Google
Home: https://gweb-research-imagen.appspot.com/
Paper: https://gweb-research-imagen.appspot.com/paper.pdf
👉 @bigdata_1
Home: https://gweb-research-imagen.appspot.com/
Paper: https://gweb-research-imagen.appspot.com/paper.pdf
👉 @bigdata_1
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Time-series Transformer Generative Adversarial Networks
Github: https://github.com/jsyoon0823/TimeGAN
Paper: https://arxiv.org/abs/2205.11164v1
Stock data: https://finance.yahoo.com/quote/GOOG/history
Energy data: http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
👉 @bigdata_1
Github: https://github.com/jsyoon0823/TimeGAN
Paper: https://arxiv.org/abs/2205.11164v1
Stock data: https://finance.yahoo.com/quote/GOOG/history
Energy data: http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
👉 @bigdata_1
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BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Github: https://github.com/adlab-autodrive/bevfusion
Paper: https://arxiv.org/abs/2205.13790v1
Dataset: https://paperswithcode.com/dataset/kitti
👉 @bigdata_1
Github: https://github.com/adlab-autodrive/bevfusion
Paper: https://arxiv.org/abs/2205.13790v1
Dataset: https://paperswithcode.com/dataset/kitti
👉 @bigdata_1
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Perturbation Augmentation for Fairer NLP
Responsible NLP projects from Meta AI.
Github: https://github.com/facebookresearch/responsiblenlp
Paper: https://arxiv.org/abs/2205.12586v1
Dataset: https://paperswithcode.com/dataset/glue
👉 @bigdata_1
Responsible NLP projects from Meta AI.
Github: https://github.com/facebookresearch/responsiblenlp
Paper: https://arxiv.org/abs/2205.12586v1
Dataset: https://paperswithcode.com/dataset/glue
👉 @bigdata_1
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Green Hierarchical Vision Transformer for Masked Image Modeling
Github: https://github.com/layneh/greenmim
Paper: https://arxiv.org/abs/2205.13515v1
Dataset: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
Github: https://github.com/layneh/greenmim
Paper: https://arxiv.org/abs/2205.13515v1
Dataset: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
🍓1
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AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Github: https://github.com/ShoufaChen/AdaptFormer
Paper: https://arxiv.org/abs/2205.13535v1
Dataset: https://paperswithcode.com/dataset/something-something-v2
👉 @bigdata_1
Github: https://github.com/ShoufaChen/AdaptFormer
Paper: https://arxiv.org/abs/2205.13535v1
Dataset: https://paperswithcode.com/dataset/something-something-v2
👉 @bigdata_1
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Surface Vision Transformers
Github: https://github.com/metrics-lab/surface-vision-transformers
Paper: https://arxiv.org/abs/2205.15836v1
👉 @bigdata_1
Github: https://github.com/metrics-lab/surface-vision-transformers
Paper: https://arxiv.org/abs/2205.15836v1
👉 @bigdata_1
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HIRL: A General Framework for Hierarchical Image Representation Learning
Github: https://github.com/hirl-team/hirl
Paper: https://arxiv.org/abs/2205.13159v1
Dataset: https://paperswithcode.com/dataset/places205
👉 @bigdata_1
Github: https://github.com/hirl-team/hirl
Paper: https://arxiv.org/abs/2205.13159v1
Dataset: https://paperswithcode.com/dataset/places205
👉 @bigdata_1
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PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
Github: https://github.com/naiyugao/panopticdepth
Paper: http://arxiv.org/abs/2206.00468
Dataset: https://paperswithcode.com/dataset/cityscapes
👉 @bigdata_1
Github: https://github.com/naiyugao/panopticdepth
Paper: http://arxiv.org/abs/2206.00468
Dataset: https://paperswithcode.com/dataset/cityscapes
👉 @bigdata_1
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MaSIF- Molecular Surface Interaction Fingerprints: Geometric deep learning to decipher patterns in protein molecular surfaces.
MaSIF is a proof-of-concept method to decipher patterns in protein surfaces important for specific biomolecular interactions.
Github: https://github.com/LPDI-EPFL/masif
Paper: https://www.nature.com/articles/s41592-019-0666-6
Data: https://github.com/LPDI-EPFL/masif#MaSIF-data-preparation
👉 @bigdata_1
MaSIF is a proof-of-concept method to decipher patterns in protein surfaces important for specific biomolecular interactions.
Github: https://github.com/LPDI-EPFL/masif
Paper: https://www.nature.com/articles/s41592-019-0666-6
Data: https://github.com/LPDI-EPFL/masif#MaSIF-data-preparation
👉 @bigdata_1
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Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark.
Github: https://github.com/naver/kapture-localization
Paper: https://arxiv.org/abs/2205.15761v1
Data: https://paperswithcode.com/dataset/inloc
👉 @bigdata_1
Github: https://github.com/naver/kapture-localization
Paper: https://arxiv.org/abs/2205.15761v1
Data: https://paperswithcode.com/dataset/inloc
👉 @bigdata_1
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Text2Human - Official PyTorch Implementation
We synthesize full-body human images starting from a given human pose
Github: https://github.com/yumingj/Text2Human
Project: https://yumingj.github.io/projects/Text2Human.html
StyleGAN: https://github.com/stylegan-human/stylegan-human
Paper: https://arxiv.org/abs/2205.15996v1
Dataset: https://github.com/yumingj/DeepFashion-MultiModal
Demo video: https://youtu.be/yKh4VORA_E0
👉 @bigdata_1
We synthesize full-body human images starting from a given human pose
Github: https://github.com/yumingj/Text2Human
Project: https://yumingj.github.io/projects/Text2Human.html
StyleGAN: https://github.com/stylegan-human/stylegan-human
Paper: https://arxiv.org/abs/2205.15996v1
Dataset: https://github.com/yumingj/DeepFashion-MultiModal
Demo video: https://youtu.be/yKh4VORA_E0
👉 @bigdata_1
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Optimizing Relevance Maps of Vision Transformers Improves Robustness
This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.
Github: https://github.com/hila-chefer/robustvit
Colab: https://colab.research.google.com/github/hila-chefer/RobustViT/blob/master/RobustViT.ipynb
Paper: https://arxiv.org/abs/2206.01161
Dataset: https://github.com/UnsupervisedSemanticSegmentation/ImageNet-S
👉 @bigdata_1
This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.
Github: https://github.com/hila-chefer/robustvit
Colab: https://colab.research.google.com/github/hila-chefer/RobustViT/blob/master/RobustViT.ipynb
Paper: https://arxiv.org/abs/2206.01161
Dataset: https://github.com/UnsupervisedSemanticSegmentation/ImageNet-S
👉 @bigdata_1
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UniSRec
The proposed approach utilizes the associated description text of items to learn transferable representations across different recommendation scenarios.
Github: https://github.com/rucaibox/unisrec
Paper: https://arxiv.org/abs/2206.05941v1
Google Drive: https://drive.google.com/drive/folders/1Uik0fMk4oquV_bS9lXTZuExAYbIDkEMW?usp=sharing
👉 @bigdata_1
The proposed approach utilizes the associated description text of items to learn transferable representations across different recommendation scenarios.
Github: https://github.com/rucaibox/unisrec
Paper: https://arxiv.org/abs/2206.05941v1
Google Drive: https://drive.google.com/drive/folders/1Uik0fMk4oquV_bS9lXTZuExAYbIDkEMW?usp=sharing
👉 @bigdata_1
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
Github: https://github.com/kssteven418/squeezeformer
Paper: https://arxiv.org/abs/2206.00888v1
Dataset: https://paperswithcode.com/dataset/librispeech
👉 @bigdata_1
Github: https://github.com/kssteven418/squeezeformer
Paper: https://arxiv.org/abs/2206.00888v1
Dataset: https://paperswithcode.com/dataset/librispeech
👉 @bigdata_1
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OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes
OntoMerger is an ontology alignment library for deduplicating knowledge graph nodes
Github: https://github.com/astrazeneca/onto_merger
Paper: https://arxiv.org/abs/2206.02238v1
Documentation: https://ontomerger.readthedocs.io/
👉 @bigdata_1
OntoMerger is an ontology alignment library for deduplicating knowledge graph nodes
Github: https://github.com/astrazeneca/onto_merger
Paper: https://arxiv.org/abs/2206.02238v1
Documentation: https://ontomerger.readthedocs.io/
👉 @bigdata_1
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CVNets: A library for training computer vision networks
Improved model, MobileViTv2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection.
Github: https://github.com/apple/ml-cvnets
Examples: https://github.com/apple/ml-cvnets/blob/main/docs/source/en/models
Paper: https://arxiv.org/abs/2206.02680v1
Dataset: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
Improved model, MobileViTv2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection.
Github: https://github.com/apple/ml-cvnets
Examples: https://github.com/apple/ml-cvnets/blob/main/docs/source/en/models
Paper: https://arxiv.org/abs/2206.02680v1
Dataset: https://paperswithcode.com/dataset/coco
👉 @bigdata_1
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EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks by Nvidia
Expressive hybrid explicit-implicit network architecture that, together with other design choices, synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry.
Github: https://github.com/NVlabs/eg3d
Project: https://nvlabs.github.io/eg3d/
Video: https://www.youtube.com/watch?v=cXxEwI7QbKg&feature=emb_logo&ab_channel=StanfordComputationalImagingLab
Paper: https://nvlabs.github.io/eg3d/media/eg3d.pdf
👉 @bigdata_1
Expressive hybrid explicit-implicit network architecture that, together with other design choices, synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry.
Github: https://github.com/NVlabs/eg3d
Project: https://nvlabs.github.io/eg3d/
Video: https://www.youtube.com/watch?v=cXxEwI7QbKg&feature=emb_logo&ab_channel=StanfordComputationalImagingLab
Paper: https://nvlabs.github.io/eg3d/media/eg3d.pdf
👉 @bigdata_1