⬆️ 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
👍15❤7🎉3
👀 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
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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
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https://www.kdnuggets.com/2022/07/5-best-places-host-data-science-portfolio.html
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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
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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
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👍12
Masked Autoencoders that Listen
Github: https://github.com/facebookresearch/audiomae
Paper: https://arxiv.org/abs/2207.06405v1
Dataset : https://paperswithcode.com/dataset/audioset
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Github: https://github.com/facebookresearch/audiomae
Paper: https://arxiv.org/abs/2207.06405v1
Dataset : https://paperswithcode.com/dataset/audioset
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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
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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
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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
👍20🔥3😱1
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
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Github: https://github.com/oatmealliu/class-incd
Paper: https://arxiv.org/abs/2207.08605v1
Dataset: https://paperswithcode.com/dataset/tiny-imagenet
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👍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
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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
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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
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👍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/
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Github: https://github.com/microsoft/distance-learner
Paper: https://arxiv.org/abs/2207.06888v1
Project: https://fast-vid2vid.github.io/
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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
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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
👍8❤4
Language Modelling with Pixels
PIXEL is a language model that operates on text rendered as images, fully removing the need for a fixed vocabulary.
Github: https://github.com/xplip/pixel
Paper: https://arxiv.org/abs/2207.06991v1
Dataset: https://paperswithcode.com/dataset/glue
Pretrained: https://huggingface.co/Team-PIXEL/pixel-base
@ai_machinelearning_big_data
PIXEL is a language model that operates on text rendered as images, fully removing the need for a fixed vocabulary.
Github: https://github.com/xplip/pixel
Paper: https://arxiv.org/abs/2207.06991v1
Dataset: https://paperswithcode.com/dataset/glue
Pretrained: https://huggingface.co/Team-PIXEL/pixel-base
@ai_machinelearning_big_data
👍20😢2
⚡️ CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS
Github: https://github.com/walkerning/aw_nas
Paper: https://arxiv.org/abs/2207.07868v1
Dataset: https://paperswithcode.com/dataset/nas-bench-201
@ai_machinelearning_big_data
Github: https://github.com/walkerning/aw_nas
Paper: https://arxiv.org/abs/2207.07868v1
Dataset: https://paperswithcode.com/dataset/nas-bench-201
@ai_machinelearning_big_data
👍15😢1
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Github: https://github.com/amirhossein-kz/hiformer
Paper: https://arxiv.org/abs/2207.08518v1
Tasks: https://paperswithcode.com/task/medical-image-segmentation
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Github: https://github.com/amirhossein-kz/hiformer
Paper: https://arxiv.org/abs/2207.08518v1
Tasks: https://paperswithcode.com/task/medical-image-segmentation
@ai_machinelearning_big_data
👍11🎉1
📝 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/
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🔧 FSD: Fully Sparse 3D Object Detection & SST: Single-stride Sparse Transformer
Github: https://github.com/tusimple/sst
Paper: http://arxiv.org/abs/2207.10035
Dataset: https://paperswithcode.com/dataset/waymo-open-dataset
@ai_machinelearning_big_data
Github: https://github.com/tusimple/sst
Paper: http://arxiv.org/abs/2207.10035
Dataset: https://paperswithcode.com/dataset/waymo-open-dataset
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👍16😱4❤3
✨ Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification
Tip-Adapter is a training-free adaption method for CLIP to conduct few-shot classification.
Github: https://github.com/gaopengcuhk/tip-adapter
Paper: https://arxiv.org/abs/2207.09519v1
Dataset: https://paperswithcode.com/dataset/oxford-102-flower
@ai_machinelearning_big_data
Tip-Adapter is a training-free adaption method for CLIP to conduct few-shot classification.
Github: https://github.com/gaopengcuhk/tip-adapter
Paper: https://arxiv.org/abs/2207.09519v1
Dataset: https://paperswithcode.com/dataset/oxford-102-flower
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