Surface Vision Transformers
Github: https://github.com/metrics-lab/surface-vision-transformers
Paper: https://arxiv.org/abs/2205.15836v1
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Github: https://github.com/metrics-lab/surface-vision-transformers
Paper: https://arxiv.org/abs/2205.15836v1
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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
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Github: https://github.com/hirl-team/hirl
Paper: https://arxiv.org/abs/2205.13159v1
Dataset: https://paperswithcode.com/dataset/places205
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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
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Github: https://github.com/naiyugao/panopticdepth
Paper: http://arxiv.org/abs/2206.00468
Dataset: https://paperswithcode.com/dataset/cityscapes
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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
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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
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🔊A Python library for audio feature extraction, classification, segmentation and applications.
Code: PyAudioAnalysis
Code: PyAudioAnalysis
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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
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Github: https://github.com/naver/kapture-localization
Paper: https://arxiv.org/abs/2205.15761v1
Data: https://paperswithcode.com/dataset/inloc
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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
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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
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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
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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
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UniSRec
The proposed approach utilizes the associated denoscription 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
The proposed approach utilizes the associated denoscription 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
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🔘 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
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Github: https://github.com/kssteven418/squeezeformer
Paper: https://arxiv.org/abs/2206.00888v1
Dataset: https://paperswithcode.com/dataset/librispeech
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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/
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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/
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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
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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
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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
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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
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🔦 Featurized Query R-CNN
Featurized object queries predicted by a query generation network in the well-established Faster R-CNN framework and develop a Featurized Query R-CN
Github: https://github.com/hustvl/featurized-queryrcnn
Paper: https://arxiv.org/abs/2206.06258v1
Dataset: https://paperswithcode.com/dataset/crowdhuman
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Featurized object queries predicted by a query generation network in the well-established Faster R-CNN framework and develop a Featurized Query R-CN
Github: https://github.com/hustvl/featurized-queryrcnn
Paper: https://arxiv.org/abs/2206.06258v1
Dataset: https://paperswithcode.com/dataset/crowdhuman
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GitHub
GitHub - hustvl/Featurized-QueryRCNN: Featurized Query R-CNN
Featurized Query R-CNN. Contribute to hustvl/Featurized-QueryRCNN development by creating an account on GitHub.
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Can CNNs Be More Robust Than Transformers?
CNN architectures without any attention-like operations that is as robust as, or even more robust than, Transformers.
Github: https://github.com/ucsc-vlaa/robustcnn
Paper: https://arxiv.org/abs/2206.03452v1
Dataset: https://paperswithcode.com/dataset/imagenet-r
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CNN architectures without any attention-like operations that is as robust as, or even more robust than, Transformers.
Github: https://github.com/ucsc-vlaa/robustcnn
Paper: https://arxiv.org/abs/2206.03452v1
Dataset: https://paperswithcode.com/dataset/imagenet-r
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@itchannels_telegram - data science, machine learning useful channels
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😲 LIVE- Towards Layer-wise Image Vectorization (CVPR 2022 Oral)
New method to progressively generate a SVG that fits the raster image in a layer-wise fashion.
Github: https://github.com/picsart-ai-research/live-layerwise-image-vectorization
Project: https://ma-xu.github.io/LIVE/
Paper: https://arxiv.org/pdf/2206.04655v1.pdf
Colab: https://colab.research.google.com/drive/1s108WmqSVH9MILOjSAu29QyAEjExOWAP?usp=sharing
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New method to progressively generate a SVG that fits the raster image in a layer-wise fashion.
Github: https://github.com/picsart-ai-research/live-layerwise-image-vectorization
Project: https://ma-xu.github.io/LIVE/
Paper: https://arxiv.org/pdf/2206.04655v1.pdf
Colab: https://colab.research.google.com/drive/1s108WmqSVH9MILOjSAu29QyAEjExOWAP?usp=sharing
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🪁 Age prediction of a speaker's voice
https://miykael.github.io/blog/2022/audio_eda_and_modeling/
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https://miykael.github.io/blog/2022/audio_eda_and_modeling/
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🔖 DoWhy | An end-to-end library for causal inference
"DoWhy" is a Python library that aims to spark causal thinking and analysis.
Github: https://github.com/py-why/dowhy
Docs: https://py-why.github.io/dowhy/
Paper: https://arxiv.org/abs/2206.06821v1
Video: https://note.microsoft.com/MSR-Webinar-DoWhy-Library-Registration-On-Demand.html
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"DoWhy" is a Python library that aims to spark causal thinking and analysis.
Github: https://github.com/py-why/dowhy
Docs: https://py-why.github.io/dowhy/
Paper: https://arxiv.org/abs/2206.06821v1
Video: https://note.microsoft.com/MSR-Webinar-DoWhy-Library-Registration-On-Demand.html
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