π 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/
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/
π6π1
[RK-Net]Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
Github: https://github.com/AggMan96/RK-Net
Paper: https://zhunzhong.site/paper/RK_Net.pdf
Dataset: https://paperswithcode.com/dataset/university-1652
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Github: https://github.com/AggMan96/RK-Net
Paper: https://zhunzhong.site/paper/RK_Net.pdf
Dataset: https://paperswithcode.com/dataset/university-1652
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π5
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
First VAE framework empowered with adaptive GPT-2s (AdaVAE).
Github: https://github.com/ImKeTT/adavae
Paper: https://arxiv.org/abs/2205.05862v1
Task: https://paperswithcode.com/task/representation-learning
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First VAE framework empowered with adaptive GPT-2s (AdaVAE).
Github: https://github.com/ImKeTT/adavae
Paper: https://arxiv.org/abs/2205.05862v1
Task: https://paperswithcode.com/task/representation-learning
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π6
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning
Github: https://github.com/explainableml/kg-sp
Paper: https://arxiv.org/abs/2205.06784v1
Dataset: https://paperswithcode.com/dataset/conceptnet
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Github: https://github.com/explainableml/kg-sp
Paper: https://arxiv.org/abs/2205.06784v1
Dataset: https://paperswithcode.com/dataset/conceptnet
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Experiments on Generalizability of User-Oriented Fairness in Recommender Systems.
Github: https://github.com/rahmanidashti/fairrecsys
Paper: https://arxiv.org/abs/2205.08289v1
Dataset: https://paperswithcode.com/dataset/movielens
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Github: https://github.com/rahmanidashti/fairrecsys
Paper: https://arxiv.org/abs/2205.08289v1
Dataset: https://paperswithcode.com/dataset/movielens
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Github: https://github.com/rampasek/GraphGPS
Paper: https://arxiv.org/abs/2205.12454v1
Dataset: https://paperswithcode.com/dataset/malnet
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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
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Github: https://github.com/KingJamesSong/DifferentiableSVD
Paper: https://arxiv.org/abs/2205.13282v1
Dataset: https://paperswithcode.com/dataset/imagenet
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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
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Github: https://github.com/wangzy22/SemAffiNet
Paper: https://arxiv.org/abs/2205.13490v1
Dataset: https://paperswithcode.com/dataset/cityscapes
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β€3π1
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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
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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
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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
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Github: https://github.com/adlab-autodrive/bevfusion
Paper: https://arxiv.org/abs/2205.13790v1
Dataset: https://paperswithcode.com/dataset/kitti
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π5
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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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
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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
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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
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Github: https://github.com/naiyugao/panopticdepth
Paper: https://arxiv.org/abs/2206.00468
Dataset: https://paperswithcode.com/dataset/cityscapes
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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
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Github: https://github.com/naiyugao/panopticdepth
Paper: https://arxiv.org/abs/2206.00806v1
Dataset: https://paperswithcode.com/dataset/kvasir-seg
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π6