π LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
Code: https://github.com/microsoft/unilm/tree/master/layoutlmv3
Paper: https://arxiv.org/abs/2204.08387v2
Dataset: https://paperswithcode.com/dataset/rvl-cdip
@ArtificialIntelligencedl
Code: https://github.com/microsoft/unilm/tree/master/layoutlmv3
Paper: https://arxiv.org/abs/2204.08387v2
Dataset: https://paperswithcode.com/dataset/rvl-cdip
@ArtificialIntelligencedl
π4
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Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation
A learning-based fully autonomous navigation framework composed of three innovative elements
Code: https://github.com/awesomericky/complex-env-navigation
Paper: https://arxiv.org/abs/2204.08647v2
Project: https://awesomericky.github.io/projects/FDM_ITS_navigation/index.html
@ArtificialIntelligencedl
A learning-based fully autonomous navigation framework composed of three innovative elements
Code: https://github.com/awesomericky/complex-env-navigation
Paper: https://arxiv.org/abs/2204.08647v2
Project: https://awesomericky.github.io/projects/FDM_ITS_navigation/index.html
@ArtificialIntelligencedl
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π₯ Fast AdvProp
Code: https://github.com/meijieru/fast_advprop
Paper: https://arxiv.org/abs/2204.09838v1
Dataset: https://paperswithcode.com/dataset/coco
@ArtificialIntelligencedl
Code: https://github.com/meijieru/fast_advprop
Paper: https://arxiv.org/abs/2204.09838v1
Dataset: https://paperswithcode.com/dataset/coco
@ArtificialIntelligencedl
π₯3π1π₯°1π1
OSSO: Obtaining Skeletal Shape from Outside (CVPR 2022)
Given a body shape with SMPL or STAR topology (in blue), we infer the underlying skeleton (in yellow).
Code: https://github.com/MarilynKeller/OSSO
Paper: https://arxiv.org/abs/2204.09838v1
Dataset: https://paperswithcode.com/dataset/agora
@ArtificialIntelligencedl
Given a body shape with SMPL or STAR topology (in blue), we infer the underlying skeleton (in yellow).
Code: https://github.com/MarilynKeller/OSSO
Paper: https://arxiv.org/abs/2204.09838v1
Dataset: https://paperswithcode.com/dataset/agora
@ArtificialIntelligencedl
π6π₯3π₯°1
π DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
Code: https://github.com/voidism/diffcse
Paper: https://arxiv.org/abs/2204.10298v1
Dataset: https://paperswithcode.com/dataset/sst
@ArtificialIntelligencedl
Code: https://github.com/voidism/diffcse
Paper: https://arxiv.org/abs/2204.10298v1
Dataset: https://paperswithcode.com/dataset/sst
@ArtificialIntelligencedl
π5π₯1
π€ Transformer-Rankers
Transformer-rankers is a library to conduct ranking experiments with transformers.
Code: https://github.com/Guzpenha/transformer_rankers
Paper: https://arxiv.org/abs/2204.10558v1
Colab: https://colab.research.google.com/drive/1wGmaO3emC7Sg-tA7nGehIQ2vjOLN9S5e?usp=sharing
@ArtificialIntelligencedl
Transformer-rankers is a library to conduct ranking experiments with transformers.
Code: https://github.com/Guzpenha/transformer_rankers
Paper: https://arxiv.org/abs/2204.10558v1
Colab: https://colab.research.google.com/drive/1wGmaO3emC7Sg-tA7nGehIQ2vjOLN9S5e?usp=sharing
@ArtificialIntelligencedl
π4
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π° Simulating Fluids in Real-World Still Images
surface-based layered representation(SLR), which decomposes the fluid and the static objects in the scene, to better synthesize the animated videos from a single fluid imagΠ΅
Code: https://github.com/generalizable-neural-performer/gnr
Paper: https://arxiv.org/abs/2204.11335
Project: https://simulatingfluids.github.io/
@ArtificialIntelligencedl
surface-based layered representation(SLR), which decomposes the fluid and the static objects in the scene, to better synthesize the animated videos from a single fluid imagΠ΅
Code: https://github.com/generalizable-neural-performer/gnr
Paper: https://arxiv.org/abs/2204.11335
Project: https://simulatingfluids.github.io/
@ArtificialIntelligencedl
π₯°6π1
β± Cross Pairwise Ranking for Unbiased Item Recommendation
Code: https://github.com/Qcactus/CPR
Paper: https://arxiv.org/abs/2204.12176v1
@ArtificialIntelligencedl
Code: https://github.com/Qcactus/CPR
Paper: https://arxiv.org/abs/2204.12176v1
@ArtificialIntelligencedl
π Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN
Code: https://github.com/wuqiuche/micromotion-stylegan
Paper: https://arxiv.org/abs/2204.12696v1
Project: https://wuqiuche.github.io/micromotion-project-page/
@ArtificialIntelligencedl
Code: https://github.com/wuqiuche/micromotion-stylegan
Paper: https://arxiv.org/abs/2204.12696v1
Project: https://wuqiuche.github.io/micromotion-project-page/
@ArtificialIntelligencedl
π3
π± NeurMips: Neural Mixture of Planar Experts for View Synthesis
A novel planar-based scene representation for modeling geometry and appearance
Code: https://github.com/zhihao-lin/neurmips
Paper: https://arxiv.org/abs/2204.13696
Project: https://zhihao-lin.github.io/neurmips/
Video: https://youtu.be/PV1dCTWL5Oo
Dataset: https://paperswithcode.com/dataset/replica
@ArtificialIntelligencedl
A novel planar-based scene representation for modeling geometry and appearance
Code: https://github.com/zhihao-lin/neurmips
Paper: https://arxiv.org/abs/2204.13696
Project: https://zhihao-lin.github.io/neurmips/
Video: https://youtu.be/PV1dCTWL5Oo
Dataset: https://paperswithcode.com/dataset/replica
@ArtificialIntelligencedl
π₯°3β€1π1
Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities from Visual SLAM
The proposed methodology creates a convex hull region by performing constrained Delaunay triangulation with depth interpolation using line features.
Code: https://github.com/url-kaist/Struct-MDC
Paper: https://arxiv.org/abs/2204.13877v1
Dataset: https://paperswithcode.com/dataset/plad-1
@ArtificialIntelligencedl
The proposed methodology creates a convex hull region by performing constrained Delaunay triangulation with depth interpolation using line features.
Code: https://github.com/url-kaist/Struct-MDC
Paper: https://arxiv.org/abs/2204.13877v1
Dataset: https://paperswithcode.com/dataset/plad-1
@ArtificialIntelligencedl
π§· Quality-Aware Decoding
Code: https://github.com/deep-spin/qaware-decode
Paper: https://arxiv.org/abs/2205.00978v1
@ArtificialIntelligencedl
Code: https://github.com/deep-spin/qaware-decode
Paper: https://arxiv.org/abs/2205.00978v1
@ArtificialIntelligencedl
π₯°3π2π1
Forwarded from Machinelearning
π OPT (Open Pre-trained Transformers) is a family of NLP models trained on billions of tokens of text obtained from the internet.
175B GPT-3
Github: https://github.com/facebookresearch/metaseq
Instructions: https://github.com/facebookresearch/metaseq/blob/main/docs/setup.md
Paper: https://arxiv.org/abs/2205.01068v2
Dataset: https://paperswithcode.com/dataset/superglue
@ai_machinelearning_big_data
175B GPT-3
Github: https://github.com/facebookresearch/metaseq
Instructions: https://github.com/facebookresearch/metaseq/blob/main/docs/setup.md
Paper: https://arxiv.org/abs/2205.01068v2
Dataset: https://paperswithcode.com/dataset/superglue
@ai_machinelearning_big_data
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πΉ Deep Video Harmonization with Color Mapping Consistency
Code: https://github.com/deep-spin/qaware-decode
Paper: https://arxiv.org/abs/2205.00687v1
Dataset: https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.
@ArtificialIntelligencedl
Code: https://github.com/deep-spin/qaware-decode
Paper: https://arxiv.org/abs/2205.00687v1
Dataset: https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.
@ArtificialIntelligencedl
π5
π Cross-view Transformers for real-time Map-view Semantic Segmentation
Code: https://github.com/bradyz/cross_view_transformers
Paper: https://arxiv.org/abs/2205.02833v1
Dataset: https://paperswithcode.com/dataset/nuscenes
@ArtificialIntelligencedl
Code: https://github.com/bradyz/cross_view_transformers
Paper: https://arxiv.org/abs/2205.02833v1
Dataset: https://paperswithcode.com/dataset/nuscenes
@ArtificialIntelligencedl
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βοΈ TorchSSL
A Pytorch-based toolbox for semi-supervised learning.
Code: https://github.com/torchssl/torchssl
Paper: https://arxiv.org/abs/2205.07246v1
Logs and weights: https://onedrive.live.com/?authkey=%21AJ%2DwKMa%2DENcbk1s&id=AF426F3217F6565A%213488&cid=AF426F3217F6565A
A Pytorch-based toolbox for semi-supervised learning.
Code: https://github.com/torchssl/torchssl
Paper: https://arxiv.org/abs/2205.07246v1
Logs and weights: https://onedrive.live.com/?authkey=%21AJ%2DwKMa%2DENcbk1s&id=AF426F3217F6565A%213488&cid=AF426F3217F6565A
π5π1
Python Data Driven Dynamics
A python package to discover SDE equation from time-series data.
Code: https://github.com/tee-lab/pydaddy
Paper: https://arxiv.org/abs/2205.02645v1
@ArtificialIntelligencedl
A python package to discover SDE equation from time-series data.
Code: https://github.com/tee-lab/pydaddy
Paper: https://arxiv.org/abs/2205.02645v1
@ArtificialIntelligencedl
π7
π€ Neural 3D Scene Reconstruction with the Manhattan-world Assumption
Code: https://github.com/zju3dv/manhattan_sdf
Paper: https://arxiv.org/abs/2205.02836
Project Page: https://zju3dv.github.io/manhattan_sdf
Video: https://www.youtube.com/watch?v=oEE7mK0YQtc
@ArtificialIntelligencedl
Code: https://github.com/zju3dv/manhattan_sdf
Paper: https://arxiv.org/abs/2205.02836
Project Page: https://zju3dv.github.io/manhattan_sdf
Video: https://www.youtube.com/watch?v=oEE7mK0YQtc
@ArtificialIntelligencedl
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β‘οΈ EmotionFlow: Capture the Dialogue Level Emotion Transitions
Code: https://github.com/fpcsong/emotionflow
Paper: https://arxiv.org/abs/2204.10298v1
Dataset: https://paperswithcode.com/dataset/meld
@ArtificialIntelligencedl
Code: https://github.com/fpcsong/emotionflow
Paper: https://arxiv.org/abs/2204.10298v1
Dataset: https://paperswithcode.com/dataset/meld
@ArtificialIntelligencedl
π5
Activating More Pixels in Image Super-Resolution Transformer
A python package to discover SDE equation from time-series data.
Code: https://github.com/chxy95/hat
Paper: https://arxiv.org/abs/2205.04437v1
Dataset: https://paperswithcode.com/dataset/bsd
@ArtificialIntelligencedl
A python package to discover SDE equation from time-series data.
Code: https://github.com/chxy95/hat
Paper: https://arxiv.org/abs/2205.04437v1
Dataset: https://paperswithcode.com/dataset/bsd
@ArtificialIntelligencedl
π2