Towards Unified Keyframe Propagation Models
A two-stream approach, where high-frequency features interact locally and low-frequency features interact globally.
Github: https://github.com/runwayml/guided-inpainting
Paper: https://arxiv.org/abs/2205.09731v1
Dataset: https://paperswithcode.com/dataset/places
@ArtificialIntelligencedl
A two-stream approach, where high-frequency features interact locally and low-frequency features interact globally.
Github: https://github.com/runwayml/guided-inpainting
Paper: https://arxiv.org/abs/2205.09731v1
Dataset: https://paperswithcode.com/dataset/places
@ArtificialIntelligencedl
π5
π A graph-transformer for whole slide image classification
Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images.
Github: https://github.com/vkola-lab/tmi2022
Paper: https://arxiv.org/abs/2205.09671v1
Dataset: https://paperswithcode.com/dataset/imagenet
@ArtificialIntelligencedl
Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images.
Github: https://github.com/vkola-lab/tmi2022
Paper: https://arxiv.org/abs/2205.09671v1
Dataset: https://paperswithcode.com/dataset/imagenet
@ArtificialIntelligencedl
π4
RankGen - Improving Text Generation with Large Ranking Models
RankGen is a 1.2 billion encoder model which maps prefixes and generations from any language model (in continutation to the prefix) to a shared vector space.
Github: https://github.com/martiansideofthemoon/rankgen
Paper: https://arxiv.org/abs/2205.09726
Dataset: https://paperswithcode.com/dataset/imagenet
@ArtificialIntelligencedl
RankGen is a 1.2 billion encoder model which maps prefixes and generations from any language model (in continutation to the prefix) to a shared vector space.
Github: https://github.com/martiansideofthemoon/rankgen
Paper: https://arxiv.org/abs/2205.09726
Dataset: https://paperswithcode.com/dataset/imagenet
@ArtificialIntelligencedl
π4
β‘οΈ Structured Attention Composition for Temporal Action Localization
Online action detection and effective and efficient exemplar-consultation mechanism
Github: https://github.com/vividle/online-action-detection
Paper: https://arxiv.org/abs/2205.09956v1
Dataset: https://paperswithcode.com/dataset/places
@ArtificialIntelligencedl
Online action detection and effective and efficient exemplar-consultation mechanism
Github: https://github.com/vividle/online-action-detection
Paper: https://arxiv.org/abs/2205.09956v1
Dataset: https://paperswithcode.com/dataset/places
@ArtificialIntelligencedl
π2
Full camouflage fixation training dataset is available!
The full camouflage fixation training dataset is available with the full fixation maps for the COD10K training dataset, which can be downloaded from: https://drive.google.com/file/d/1inb5iNTDswFPDm4SpzBbVgZdI4puAv_3/view?usp=sharing
Github: https://github.com/JingZhang617/COD-Rank-Localize-and-Segment
Paper: https://arxiv.org/abs/2205.11333v1
Dataset: https://paperswithcode.com/dataset/salicon
@ArtificialIntelligencedl
The full camouflage fixation training dataset is available with the full fixation maps for the COD10K training dataset, which can be downloaded from: https://drive.google.com/file/d/1inb5iNTDswFPDm4SpzBbVgZdI4puAv_3/view?usp=sharing
Github: https://github.com/JingZhang617/COD-Rank-Localize-and-Segment
Paper: https://arxiv.org/abs/2205.11333v1
Dataset: https://paperswithcode.com/dataset/salicon
@ArtificialIntelligencedl
π3
ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
Github: https://github.com/difanliu/asset
Paper: https://arxiv.org/abs/2205.12231v1
Dataset: https://paperswithcode.com/dataset/ade20k
Pretrained model: https://www.dropbox.com/s/5kyov71ko340ra0/landscape.zip?dl=0
@ArtificialIntelligencedl
Github: https://github.com/difanliu/asset
Paper: https://arxiv.org/abs/2205.12231v1
Dataset: https://paperswithcode.com/dataset/ade20k
Pretrained model: https://www.dropbox.com/s/5kyov71ko340ra0/landscape.zip?dl=0
@ArtificialIntelligencedl
π4
Recipe for a General, Powerful, Scalable Graph Transformer
Github: https://github.com/rampasek/GraphGPS
Paper: https://arxiv.org/abs/2205.12454v1
Dataset: https://paperswithcode.com/dataset/malnet
@ArtificialIntelligencedl
Github: https://github.com/rampasek/GraphGPS
Paper: https://arxiv.org/abs/2205.12454v1
Dataset: https://paperswithcode.com/dataset/malnet
@ArtificialIntelligencedl
π₯3
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
Github: https://github.com/KingJamesSong/DifferentiableSVD
Paper: https://arxiv.org/abs/2205.13282v1
Dataset: https://paperswithcode.com/dataset/imagenet
@ArtificialIntelligencedl
Github: https://github.com/KingJamesSong/DifferentiableSVD
Paper: https://arxiv.org/abs/2205.13282v1
Dataset: https://paperswithcode.com/dataset/imagenet
@ArtificialIntelligencedl
π6
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation
Github: https://github.com/wangzy22/SemAffiNet
Paper: https://arxiv.org/abs/2205.13490v1
Dataset: https://paperswithcode.com/dataset/cityscapes
@ArtificialIntelligencedl
Github: https://github.com/wangzy22/SemAffiNet
Paper: https://arxiv.org/abs/2205.13490v1
Dataset: https://paperswithcode.com/dataset/cityscapes
@ArtificialIntelligencedl
β€3π1
This media is not supported in your browser
VIEW IN TELEGRAM
CMA-ES with Margin
CMA-ES with Margin (CMA-ESwM) [1] is a CMA-ES variant proposed for mixed-integer black-box optimization, which introduces a lower bound on the marginal probability associated with integer variables.
Github: https://github.com/evoconjp/cma-es_with_margin
Paper: https://arxiv.org/abs/2205.13482v1
@ArtificialIntelligencedl
CMA-ES with Margin (CMA-ESwM) [1] is a CMA-ES variant proposed for mixed-integer black-box optimization, which introduces a lower bound on the marginal probability associated with integer variables.
Github: https://github.com/evoconjp/cma-es_with_margin
Paper: https://arxiv.org/abs/2205.13482v1
@ArtificialIntelligencedl
π5
π» 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
@ArtificialIntelligencedl
Github: https://github.com/adlab-autodrive/bevfusion
Paper: https://arxiv.org/abs/2205.13790v1
Dataset: https://paperswithcode.com/dataset/kitti
@ArtificialIntelligencedl
π5
Surface Vision Transformers
Github: https://github.com/metrics-lab/surface-vision-transformers
Paper: https://arxiv.org/abs/2205.15836v1
@ArtificialIntelligencedl
Github: https://github.com/metrics-lab/surface-vision-transformers
Paper: https://arxiv.org/abs/2205.15836v1
@ArtificialIntelligencedl
β€4
Good Intentions: Adaptive Parameter Servers via Intent Signaling
AdaPS is efficient for many machine learning tasks out of the box because it automatically adapts to the underlying task
Github: https://github.com/alexrenz/adaps
Paper: https://arxiv.org/abs/2206.00470v1
Docs: https://github.com/alexrenz/AdaPS/blob/vldb20/docs/experiments-vldb20.md
@ArtificialIntelligencedl
AdaPS is efficient for many machine learning tasks out of the box because it automatically adapts to the underlying task
Github: https://github.com/alexrenz/adaps
Paper: https://arxiv.org/abs/2206.00470v1
Docs: https://github.com/alexrenz/AdaPS/blob/vldb20/docs/experiments-vldb20.md
@ArtificialIntelligencedl
π6
π PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
Github: https://github.com/naiyugao/panopticdepth
Paper: https://arxiv.org/abs/2206.00468
Dataset: https://paperswithcode.com/dataset/cityscapes
@ArtificialIntelligencedl
Github: https://github.com/naiyugao/panopticdepth
Paper: https://arxiv.org/abs/2206.00468
Dataset: https://paperswithcode.com/dataset/cityscapes
@ArtificialIntelligencedl
π5
β© XBound-Former: Toward Cross-scale Boundary Modeling in Transformers
Github: https://github.com/naiyugao/panopticdepth
Paper: https://arxiv.org/abs/2206.00806v1
Dataset: https://paperswithcode.com/dataset/kvasir-seg
@ArtificialIntelligencedl
Github: https://github.com/naiyugao/panopticdepth
Paper: https://arxiv.org/abs/2206.00806v1
Dataset: https://paperswithcode.com/dataset/kvasir-seg
@ArtificialIntelligencedl
π6
Invertible Neural Networks for Graph Prediction
Github: https://github.com/hamrel-cxu/invertible-graph-neural-network-ignn
Paper: https://arxiv.org/abs/2206.01163v1
@ArtificialIntelligencedl
Github: https://github.com/hamrel-cxu/invertible-graph-neural-network-ignn
Paper: https://arxiv.org/abs/2206.01163v1
@ArtificialIntelligencedl
π4β€1
SNAKE: Shape-aware Neural 3D Keypoint Field
Github: https://github.com/zhongcl-thu/snake
Paper: https://paperswithcode.com/dataset/modelnet
@ArtificialIntelligencedl
Github: https://github.com/zhongcl-thu/snake
Paper: https://paperswithcode.com/dataset/modelnet
@ArtificialIntelligencedl
β€4
Forwarded from Python/ django
πA Python library for audio feature extraction, classification, segmentation and applications.
Code: PyAudioAnalysis
#Python #Audio #Analyzer
@pythonl
Code: PyAudioAnalysis
#Python #Audio #Analyzer
@pythonl
π5β€1
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/
@ArtificialIntelligencedl
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/
@ArtificialIntelligencedl
π6