FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
Github: https://github.com/timothyhtimothy/fast-vqa
Paper: https://arxiv.org/abs/2207.02595v1
Dataset: https://paperswithcode.com/dataset/kinetics
Github: https://github.com/timothyhtimothy/fast-vqa
Paper: https://arxiv.org/abs/2207.02595v1
Dataset: https://paperswithcode.com/dataset/kinetics
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🔌 Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations
For the first time brings the power of robust data augmentations into regularizing the NeRF training.
Github: https://github.com/vita-group/aug-nerf
Paper: https://arxiv.org/abs/2207.01164v1
Cloud Drive: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
@ai_machinelearning_big_data
For the first time brings the power of robust data augmentations into regularizing the NeRF training.
Github: https://github.com/vita-group/aug-nerf
Paper: https://arxiv.org/abs/2207.01164v1
Cloud Drive: https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1
@ai_machinelearning_big_data
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Bounding Box Deep Learning: The Future of Video Annotation
https://www.kdnuggets.com/2022/07/bounding-box-deep-learning-future-video-annotation.html
@ai_machinelearning_big_data
https://www.kdnuggets.com/2022/07/bounding-box-deep-learning-future-video-annotation.html
@ai_machinelearning_big_data
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🔥 SeqDeepFake: Detecting and Recovering Sequential DeepFake Manipulation
First Seq-DeepFake dataset, where face images are manipulated sequentially with corresponding annotations of sequential facial manipulation vectors.
Github: https://github.com/rshaojimmy/seqdeepfake
Project: https://rshaojimmy.github.io/Projects/SeqDeepFake
Paper: https://arxiv.org/pdf/2207.02204.pdf
Dataset: https://paperswithcode.com/dataset/imagenet
@ai_machinelearning_big_data
First Seq-DeepFake dataset, where face images are manipulated sequentially with corresponding annotations of sequential facial manipulation vectors.
Github: https://github.com/rshaojimmy/seqdeepfake
Project: https://rshaojimmy.github.io/Projects/SeqDeepFake
Paper: https://arxiv.org/pdf/2207.02204.pdf
Dataset: https://paperswithcode.com/dataset/imagenet
@ai_machinelearning_big_data
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Understanding the Design of a Convolutional Neural Network
https://machinelearningmastery.com/understanding-the-design-of-a-convolutional-neural-network/
@ai_machinelearning_big_data
https://machinelearningmastery.com/understanding-the-design-of-a-convolutional-neural-network/
@ai_machinelearning_big_data
👍11
🔀 No Language Left Behind
Meta's open-sources models capable of delivering high-quality translations directly between any pair of 200+ languages.
Github: https://github.com/facebookresearch/fairseq/tree/nllb
Paper: https://research.facebook.com/publications/no-language-left-behind/
Website: https://ai.facebook.com/research/no-language-left-behind/
Demo: https://nllb.metademolab.com/
@ai_machinelearning_big_data
Meta's open-sources models capable of delivering high-quality translations directly between any pair of 200+ languages.
Github: https://github.com/facebookresearch/fairseq/tree/nllb
Paper: https://research.facebook.com/publications/no-language-left-behind/
Website: https://ai.facebook.com/research/no-language-left-behind/
Demo: https://nllb.metademolab.com/
@ai_machinelearning_big_data
👍10
⬆️ YOLOv7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Github: https://github.com/wongkinyiu/yolov7
Paper: https://arxiv.org/abs/2207.02696v1
Dataset: https://paperswithcode.com/dataset/coco
@ai_machinelearning_big_data
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Github: https://github.com/wongkinyiu/yolov7
Paper: https://arxiv.org/abs/2207.02696v1
Dataset: https://paperswithcode.com/dataset/coco
@ai_machinelearning_big_data
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👀 Object Centric Open Vocabulary Detection
Object-centric alignment of the language embeddings from the CLIP model.
Github: https://github.com/hanoonaR/object-centric-ovd
Paper: https://arxiv.org/abs/2207.03482v1
Dataset: https://paperswithcode.com/dataset/imagenet
@ai_machinelearning_big_data
Object-centric alignment of the language embeddings from the CLIP model.
Github: https://github.com/hanoonaR/object-centric-ovd
Paper: https://arxiv.org/abs/2207.03482v1
Dataset: https://paperswithcode.com/dataset/imagenet
@ai_machinelearning_big_data
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The 5 Best Places To Host Your Data Science Portfolio
https://www.kdnuggets.com/2022/07/5-best-places-host-data-science-portfolio.html
@ai_machinelearning_big_data
https://www.kdnuggets.com/2022/07/5-best-places-host-data-science-portfolio.html
@ai_machinelearning_big_data
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🔸 An Efficiency Study for SPLADE Models
SParse Lexical AnD Expansion Model for First Stage Ranking.
Github: https://github.com/naver/splade
Paper: https://arxiv.org/abs/2207.03834v1
Dataset: https://paperswithcode.com/dataset/ms-marco
@ai_machinelearning_big_data
SParse Lexical AnD Expansion Model for First Stage Ranking.
Github: https://github.com/naver/splade
Paper: https://arxiv.org/abs/2207.03834v1
Dataset: https://paperswithcode.com/dataset/ms-marco
@ai_machinelearning_big_data
👍12
Masked Autoencoders that Listen
Github: https://github.com/facebookresearch/audiomae
Paper: https://arxiv.org/abs/2207.06405v1
Dataset : https://paperswithcode.com/dataset/audioset
@ai_machinelearning_big_data
Github: https://github.com/facebookresearch/audiomae
Paper: https://arxiv.org/abs/2207.06405v1
Dataset : https://paperswithcode.com/dataset/audioset
@ai_machinelearning_big_data
GitHub
GitHub - facebookresearch/AudioMAE: This repo hosts the code and models of "Masked Autoencoders that Listen".
This repo hosts the code and models of "Masked Autoencoders that Listen". - facebookresearch/AudioMAE
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🧍♂ PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision
Human-centric privacy-preserving synthetic data generator with highly parametrized domain randomization.
Github: https://github.com/unity-technologies/peoplesanspeople
Paper: https://arxiv.org/abs/2207.05025v1
Demo Video: https://www.youtube.com/watch?v=mQ_DUdB70dc
@ai_machinelearning_big_data
Human-centric privacy-preserving synthetic data generator with highly parametrized domain randomization.
Github: https://github.com/unity-technologies/peoplesanspeople
Paper: https://arxiv.org/abs/2207.05025v1
Demo Video: https://www.youtube.com/watch?v=mQ_DUdB70dc
@ai_machinelearning_big_data
👍13
The AIoT Revolution: How AI and IoT Are Transforming Our World
https://www.kdnuggets.com/2022/07/aiot-revolution-ai-iot-transforming-world.html
@ai_machinelearning_big_data
https://www.kdnuggets.com/2022/07/aiot-revolution-ai-iot-transforming-world.html
@ai_machinelearning_big_data
🏎 Instance Shadow Detection with A Single-Stage Detector
Deep framework, and an evaluation metric to approach this new task.
Github: https://github.com/stevewongv/InstanceShadowDetection
Instance Shadow Detection: https://github.com/stevewongv/SSIS
Video: https://www.youtube.com/watch?v=p0b_2SsFypw
Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing
Paper: https://arxiv.org/abs/2207.04614v1
Datasets: https://paperswithcode.com/dataset/soba
@ai_machinelearning_big_data
Deep framework, and an evaluation metric to approach this new task.
Github: https://github.com/stevewongv/InstanceShadowDetection
Instance Shadow Detection: https://github.com/stevewongv/SSIS
Video: https://www.youtube.com/watch?v=p0b_2SsFypw
Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing
Paper: https://arxiv.org/abs/2207.04614v1
Datasets: https://paperswithcode.com/dataset/soba
@ai_machinelearning_big_data
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Class-incremental Novel Class Discovery
Github: https://github.com/oatmealliu/class-incd
Paper: https://arxiv.org/abs/2207.08605v1
Dataset: https://paperswithcode.com/dataset/tiny-imagenet
@ai_machinelearning_big_data
Github: https://github.com/oatmealliu/class-incd
Paper: https://arxiv.org/abs/2207.08605v1
Dataset: https://paperswithcode.com/dataset/tiny-imagenet
@ai_machinelearning_big_data
👍12
KGI (Knowledge Graph Induction) for slot filling
Github: https://github.com/ibm/kgi-slot-filling
KILT data and knowledge source: https://github.com/facebookresearch/KILT
Paper: https://arxiv.org/abs/2207.06300v1
Dataset: https://paperswithcode.com/dataset/natural-questions
@ai_machinelearning_big_data
Github: https://github.com/ibm/kgi-slot-filling
KILT data and knowledge source: https://github.com/facebookresearch/KILT
Paper: https://arxiv.org/abs/2207.06300v1
Dataset: https://paperswithcode.com/dataset/natural-questions
@ai_machinelearning_big_data
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🎯 Object-Compositional Neural Implicit Surfaces
Github: https://github.com/qianyiwu/objsdf
Paper: https://arxiv.org/abs/2207.09686v1
Project: https://qianyiwu.github.io/objectsdf/
Dataset: https://paperswithcode.com/dataset/scannet
Github: https://github.com/qianyiwu/objsdf
Paper: https://arxiv.org/abs/2207.09686v1
Project: https://qianyiwu.github.io/objectsdf/
Dataset: https://paperswithcode.com/dataset/scannet
GitHub
GitHub - QianyiWu/objsdf: :t-rex: [ECCV‘22] Pytorch implementation of 'Object-Compositional Neural Implicit Surfaces'
:t-rex: [ECCV‘22] Pytorch implementation of 'Object-Compositional Neural Implicit Surfaces' - QianyiWu/objsdf
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Benchmarking Omni-Vision Representation through the Lens of Visual Realms
Github: https://github.com/ZhangYuanhan-AI/OmniBenchmark
Project: https://zhangyuanhan-ai.github.io/OmniBenchmark
Paper: https://arxiv.org/abs/2207.07106v1
Competition: https://codalab.lisn.upsaclay.fr/competitions/6043
@ai_machinelearning_big_data
Github: https://github.com/ZhangYuanhan-AI/OmniBenchmark
Project: https://zhangyuanhan-ai.github.io/OmniBenchmark
Paper: https://arxiv.org/abs/2207.07106v1
Competition: https://codalab.lisn.upsaclay.fr/competitions/6043
@ai_machinelearning_big_data
👍10
Distance Learner: Incorporating Manifold Prior to Model Training
Github: https://github.com/microsoft/distance-learner
Paper: https://arxiv.org/abs/2207.06888v1
Project: https://fast-vid2vid.github.io/
@ai_machinelearning_big_data
Github: https://github.com/microsoft/distance-learner
Paper: https://arxiv.org/abs/2207.06888v1
Project: https://fast-vid2vid.github.io/
@ai_machinelearning_big_data
GitHub
GitHub - microsoft/distance-learner: Official implementation for "Distance Learner: Incorporating Manifold Prior to Model Training"
Official implementation for "Distance Learner: Incorporating Manifold Prior to Model Training" - microsoft/distance-learner
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🚀 Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting
Github: https://github.com/hikopensource/davar-lab-ocr
Paper: https://arxiv.org/abs/2207.06694v1
Dataset: https://paperswithcode.com/dataset/total-text
@ai_machinelearning_big_data
Github: https://github.com/hikopensource/davar-lab-ocr
Paper: https://arxiv.org/abs/2207.06694v1
Dataset: https://paperswithcode.com/dataset/total-text
@ai_machinelearning_big_data
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