NAFSSR: Stereo Image Super-Resolution Using NAFNet

Github: https://github.com/megvii-research/NAFNet

Paper: https://arxiv.org/abs/2204.08714v1

Demo: https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing

Dataset: https://paperswithcode.com/dataset/kitti

@computer_science_and_programming

🧊 Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)

Github: https://github.com/dvlab-research/focalsconv

Paper: https://arxiv.org/abs/2204.12463

Dataset: https://paperswithcode.com/dataset/nuscenes

@computer_science_and_programming

AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Github: https://github.com/ShoufaChen/AdaptFormer

Paper: https://arxiv.org/abs/2205.13535v1

Dataset: https://paperswithcode.com/dataset/something-something-v2

@computer_science_and_programming

Squeezeformer: An Efficient Transformer for Automatic Speech Recognition

Github: https://github.com/kssteven418/squeezeformer

Paper: https://arxiv.org/abs/2206.00888v1

Dataset: https://paperswithcode.com/dataset/librispeech

@computer_science_and_programming

MIT, Introduction to Deep Learning, 2022 Lecture series

Website:
http://introtodeeplearning.com/

Lecture:
https://www.youtube.com/watch?v=7sB052Pz0sQ&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

@computer_science_and_programming

Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech

IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models.

Github: https://github.com/DigitalPhonetics/IMS-Toucan
https://github.com/rballester/tntorch

Pre-Generated Audios: https://multilingualtoucan.github.io/

Cloning prosody across speakers: https://toucanprosodycloningdemo.github.io/

Interactive Demo: https://huggingface.co/spaces/Flux9665/IMS-Toucan

Paper: https://arxiv.org/abs/2206.12229v1

@computer_science_and_programming

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

@computer_science_and_programming

Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation.

Github: https://github.com/164140757/scm

Paper: https://arxiv.org/abs/2207.10447v1

Dataset: https://paperswithcode.com/dataset/cub-200-2011

@computer_science_and_programming

UFO: segmentation 140+ FPS

👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!


𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified framework for co-segmentation
✅Co-segmentation, co-saliency, saliency
✅Block for long-range dependencies
✅Able to reach for 140 FPS in inference
✅The new SOTA on multiple datasets

Paper:
https://arxiv.org/pdf/2203.04708v2.pdf

Code:
https://github.com/suyukun666/UFO


@computer_science_and_programming

Harvard CS109A #DataScience course materials — huge collection free & open!

1. Lecture notes
2. R code, #Python notebooks
3. Lab material
4. Advanced sections
and more ...

https://harvard-iacs.github.io/2019-CS109A/pages/materials.html

@computer_science_and_programming

Resources for performing deep learning on satellite imagery:
- Techniques
- Datasets
- ML best Practice
- Courses
and more ...

@computer_science_and_programming

Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

Paper:
https://arxiv.org/pdf/2207.10660.pdf

Github:
https://github.com/facebookresearch/omni3d

Project page:
https://garrickbrazil.com/omni3d/


@computer_science_and_programming

You don't need to spend several $𝟭𝟬𝟬𝟬𝘀 to learn Data Science.❌

Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.💥

Here's 8 free Courses that'll teach you better than the paid ones:


1. CS50’s Introduction to Artificial Intelligence with Python (Harvard)

https://lnkd.in/d9CkkfGK

2. Data Science: Machine Learning (Harvard)

https://lnkd.in/dQ7zkCv9

3. Artificial Intelligence (MIT)

https://lnkd.in/dG5BCPen

4. Introduction to Computational Thinking and Data Science (MIT)

https://lnkd.in/ddm5Ckk9

5. Machine Learning (MIT)

https://lnkd.in/dJEjStCw

6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)

https://lnkd.in/dkpyt6qr

7. Statistical Learning (Stanford)

https://lnkd.in/dymn4hbD

8. Mining Massive Data Sets (Stanford)

📍https://lnkd.in/d2uf-FkB

@computer_science_and_programming

SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation


The dataset consists of unlabeled patch triplets from 251,079 locations across the globe, each patch covering 2640m x 2640m and including 4 seasonal time stamps.

Github:
https://github.com/zhu-xlab/ssl4eo-s12

Paper:
https://arxiv.org/abs/2211.07044v1

Dataset:
https://mediatum.ub.tum.de/1660427

@computer_science_and_programming

Automatically find and fix errors in any ML datasets with cleanlab

This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training.

Github:
https://github.com/cleanlab/cleanlab

Docs:
https://docs.cleanlab.ai/stable/index.html

Examples:
https://github.com/cleanlab/examples

Paper:
https://arxiv.org/abs/2211.13895v1

👉 @computer_science_and_programming

DiffusionInst: Diffusion Model for Instance Segmentation

* DiffusionInst is the first work of diffusion model for instance segmentation

Github:
https://github.com/chenhaoxing/DiffusionInst

Paper:
https://arxiv.org/abs/2212.02773v2

Getting started:
https://github.com/chenhaoxing/DiffusionInst/blob/main/GETTING_STARTED.md

Dataset:
https://paperswithcode.com/dataset/lvis


@computer_science_and_programming