π Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives
a training paradigm for INRs whose target output is image pixels, to encode image derivatives in addition to image values in the neural network.
Github: https://github.com/megvii-research/Sobolev_INRs
Paper: https://arxiv.org/abs/2207.10395v1
Dataset: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
@ArtificialIntelligencedl
a training paradigm for INRs whose target output is image pixels, to encode image derivatives in addition to image values in the neural network.
Github: https://github.com/megvii-research/Sobolev_INRs
Paper: https://arxiv.org/abs/2207.10395v1
Dataset: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
@ArtificialIntelligencedl
π2
Forwarded from Machinelearning
π§Ώ Generative Multiplane Images: Making a 2D GAN 3D-Aware
What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator.
Github: https://github.com/apple/ml-gmpi
Paper: https://arxiv.org/abs/2207.10642v1
Dataset: https://paperswithcode.com/dataset/metfaces
Project: https://xiaoming-zhao.github.io/projects/gmpi/
Pretrained checkpoints: https://drive.google.com/drive/folders/1MEIjen0XOIW-kxEMfBUONnKYrkRATSR_
@ai_machinelearning_big_data
What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator.
Github: https://github.com/apple/ml-gmpi
Paper: https://arxiv.org/abs/2207.10642v1
Dataset: https://paperswithcode.com/dataset/metfaces
Project: https://xiaoming-zhao.github.io/projects/gmpi/
Pretrained checkpoints: https://drive.google.com/drive/folders/1MEIjen0XOIW-kxEMfBUONnKYrkRATSR_
@ai_machinelearning_big_data
π4β€1
Grounding Visual Representations with Texts for Domain Generalization
Github: https://github.com/mswzeus/gvrt
Paper: https://arxiv.org/abs/2207.10285v1
Dataset: https://paperswithcode.com/dataset/pacs
@ArtificialIntelligencedl
Github: https://github.com/mswzeus/gvrt
Paper: https://arxiv.org/abs/2207.10285v1
Dataset: https://paperswithcode.com/dataset/pacs
@ArtificialIntelligencedl
π4
AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection
Github: https://github.com/zehuichen123/autoalignv2
Paper: https://arxiv.org/abs/2207.10316v1
Dataset: https://paperswithcode.com/dataset/nuscenes
@ArtificialIntelligencedl
Github: https://github.com/zehuichen123/autoalignv2
Paper: https://arxiv.org/abs/2207.10316v1
Dataset: https://paperswithcode.com/dataset/nuscenes
@ArtificialIntelligencedl
π8
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π±ββοΈ Multiface: A Dataset for Neural Face Rendering
Github: https://github.com/facebookresearch/multiface
Paper: https://arxiv.org/abs/2207.11243v1
Dataset: https://paperswithcode.com/dataset/facewarehouse
@ArtificialIntelligencedl
Github: https://github.com/facebookresearch/multiface
Paper: https://arxiv.org/abs/2207.11243v1
Dataset: https://paperswithcode.com/dataset/facewarehouse
@ArtificialIntelligencedl
π12β€1
β
C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning
Circular convolution-based batch-wise compression for SL (C3-SL) to compress multiple features into one single feature.
Github: https://github.com/WesleyHsieh0806/C3-SL
Paper: https://arxiv.org/abs/2207.12397v1
Dataset: https://github.com/WesleyHsieh0806/C3-SL#books-prepare-dataset
Pretrained Dataset: https://github.com/WesleyHsieh0806/C3-SL/blob/master/Pretrained_Dataset.md
@ArtificialIntelligencedl
Circular convolution-based batch-wise compression for SL (C3-SL) to compress multiple features into one single feature.
Github: https://github.com/WesleyHsieh0806/C3-SL
Paper: https://arxiv.org/abs/2207.12397v1
Dataset: https://github.com/WesleyHsieh0806/C3-SL#books-prepare-dataset
Pretrained Dataset: https://github.com/WesleyHsieh0806/C3-SL/blob/master/Pretrained_Dataset.md
@ArtificialIntelligencedl
π2
π Compositional Human-Scene Interaction Synthesis with Semantic Control (COINS)
Github: https://github.com/zkf1997/coins
Paper: https://arxiv.org/abs/2207.12824v1
Project: https://zkf1997.github.io/COINS/index.html
@ArtificialIntelligencedl
Github: https://github.com/zkf1997/coins
Paper: https://arxiv.org/abs/2207.12824v1
Project: https://zkf1997.github.io/COINS/index.html
@ArtificialIntelligencedl
β€8
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
Github: https://github.com/huixiancheng/cenet
Paper: https://arxiv.org/abs/2207.12691v1
Project: https://paperswithcode.com/dataset/semantickitti
@ArtificialIntelligencedl
Github: https://github.com/huixiancheng/cenet
Paper: https://arxiv.org/abs/2207.12691v1
Project: https://paperswithcode.com/dataset/semantickitti
@ArtificialIntelligencedl
π5π€1
Identifying Hard Noise in Long-Tailed Sample Distribution
Github: https://github.com/yxymessi/h2e-framework
Paper: https://arxiv.org/abs/2207.13378v1
Dataset: https://paperswithcode.com/dataset/food-101
@ArtificialIntelligencedl
Github: https://github.com/yxymessi/h2e-framework
Paper: https://arxiv.org/abs/2207.13378v1
Dataset: https://paperswithcode.com/dataset/food-101
@ArtificialIntelligencedl
π₯6
Visual Recognition by Request
Github: https://github.com/chufengt/Visual-Recognition-by-Request
Paper: https://arxiv.org/coming_soon
Dataset: https://paperswithcode.com/dataset/cityscapes-panoptic-parts
@ArtificialIntelligencedl
Github: https://github.com/chufengt/Visual-Recognition-by-Request
Paper: https://arxiv.org/coming_soon
Dataset: https://paperswithcode.com/dataset/cityscapes-panoptic-parts
@ArtificialIntelligencedl
GitHub
GitHub - chufengt/ViRReq: Code for the paper "Visual Recognition by Request".
Code for the paper "Visual Recognition by Request". - GitHub - chufengt/ViRReq: Code for the paper "Visual Recognition by Request".
π7
π§ββ Body-Part Map for Interactiveness
body-part saliency maps to mine informative cues from not only the targeted person, but also other persons in the image.
Github: https://github.com/enlighten0707/body-part-map-for-interactiveness
Paper: https://arxiv.org/abs/2207.14192v1
Dataset: https://paperswithcode.com/dataset/hico-det
@ArtificialIntelligencedl
body-part saliency maps to mine informative cues from not only the targeted person, but also other persons in the image.
Github: https://github.com/enlighten0707/body-part-map-for-interactiveness
Paper: https://arxiv.org/abs/2207.14192v1
Dataset: https://paperswithcode.com/dataset/hico-det
@ArtificialIntelligencedl
π7π₯2
π§ββ Body-Part Map for Interactiveness
new lighting estimation and editing framework to generate high-dynamic-range (HDR) indoor panorama lighting
Github: https://github.com/wanggcong/stylelight
Paper: https://arxiv.org/abs/2207.14811v1
Video: https://www.youtube.com/watch?v=sHeWK1MSPg4
Model: https://drive.google.com/file/d/1vHfwrtgk0EjZlS14Ye5lASJ0R4IWl_4w/view?usp=sharing
Project: https://style-light.github.io/
Dataset: https://indoor.hdrdb.com/
@ArtificialIntelligencedl
new lighting estimation and editing framework to generate high-dynamic-range (HDR) indoor panorama lighting
Github: https://github.com/wanggcong/stylelight
Paper: https://arxiv.org/abs/2207.14811v1
Video: https://www.youtube.com/watch?v=sHeWK1MSPg4
Model: https://drive.google.com/file/d/1vHfwrtgk0EjZlS14Ye5lASJ0R4IWl_4w/view?usp=sharing
Project: https://style-light.github.io/
Dataset: https://indoor.hdrdb.com/
@ArtificialIntelligencedl
π6π₯2
π Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning
Github: https://github.com/andrejorsula/drl_grasping
Paper: https://arxiv.org/abs/2208.00818v1
@ArtificialIntelligencedl
Github: https://github.com/andrejorsula/drl_grasping
Paper: https://arxiv.org/abs/2208.00818v1
@ArtificialIntelligencedl
π₯7
π¦Ύ PyABSA - Open Framework for Aspect-based Sentiment Analysis
Github: https://github.com/yangheng95/pyabsa
Paper: https://arxiv.org/abs/2208.01368v1
Dataset: https://paperswithcode.com/dataset/sst
Colab: https://github.com/yangheng95/PyABSA/blob/release/readme/tutorial_readme.md
@ArtificialIntelligencedl
Github: https://github.com/yangheng95/pyabsa
Paper: https://arxiv.org/abs/2208.01368v1
Dataset: https://paperswithcode.com/dataset/sst
Colab: https://github.com/yangheng95/PyABSA/blob/release/readme/tutorial_readme.md
@ArtificialIntelligencedl
π6
π Negative Frames Matter in Egocentric Visual Query 2D Localization
Github: https://github.com/facebookresearch/vq2d_cvpr
Paper: https://arxiv.org/abs/2208.01949v1
VQ2D baseline: https://github.com/EGO4D/episodic-memory/tree/main/VQ2D
@ArtificialIntelligencedl
Github: https://github.com/facebookresearch/vq2d_cvpr
Paper: https://arxiv.org/abs/2208.01949v1
VQ2D baseline: https://github.com/EGO4D/episodic-memory/tree/main/VQ2D
@ArtificialIntelligencedl
π7
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Prompt Tuning for Generative Multimodal Pretrained Models
Github: https://github.com/ofa-sys/ofa
Paper: https://arxiv.org/abs/2208.02532v1
Dataset: https://paperswithcode.com/dataset/snli-ve
Demo: https://huggingface.co/spaces/OFA-Sys/OFA-Generic_Interface
@ArtificialIntelligencedl
Github: https://github.com/ofa-sys/ofa
Paper: https://arxiv.org/abs/2208.02532v1
Dataset: https://paperswithcode.com/dataset/snli-ve
Demo: https://huggingface.co/spaces/OFA-Sys/OFA-Generic_Interface
@ArtificialIntelligencedl
π₯7
Constructing Balance from Imbalance for Long-tailed Image Recognition
Github: https://github.com/silicx/dlsa
Paper: https://arxiv.org/abs/2208.02567v1
Dataset: https://paperswithcode.com/dataset/places
@ArtificialIntelligencedl
Github: https://github.com/silicx/dlsa
Paper: https://arxiv.org/abs/2208.02567v1
Dataset: https://paperswithcode.com/dataset/places
@ArtificialIntelligencedl
π7
β Learning Prior Feature and Attention Enhanced Image Inpainting
Github: https://github.com/ewrfcas/MAE-FAR
Paper: https://arxiv.org/abs/2208.01837v1
Dataset: https://paperswithcode.com/dataset/places
@ArtificialIntelligencedl
Github: https://github.com/ewrfcas/MAE-FAR
Paper: https://arxiv.org/abs/2208.01837v1
Dataset: https://paperswithcode.com/dataset/places
@ArtificialIntelligencedl
π10
The Complete Collection of Data Science Projects
https://www.kdnuggets.com/2022/08/complete-collection-data-science-projects-part-1.html
@ArtificialIntelligencedl
https://www.kdnuggets.com/2022/08/complete-collection-data-science-projects-part-1.html
@ArtificialIntelligencedl
π7
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition
Named entity recognition (NER) is the task to detect and classify the entity spans in the text.
Github: https://github.com/yhcc/cnn_nested_ner
Paper: https://arxiv.org/abs/2208.04534v1
Dataset: https://paperswithcode.com/dataset/genia
@ArtificialIntelligencedl
Named entity recognition (NER) is the task to detect and classify the entity spans in the text.
Github: https://github.com/yhcc/cnn_nested_ner
Paper: https://arxiv.org/abs/2208.04534v1
Dataset: https://paperswithcode.com/dataset/genia
@ArtificialIntelligencedl
π₯5π2β€1