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

DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

DeepLSD is a generic line detector that combines the robustness of deep learning with the accuracy of handcrafted detectors. It can be used to extract generic line segments from images in-the-wild, and is suitable for any task requiring high precision, such as homography estimation, visual localization, and 3D reconstruction. By predicting a line distance and angle fields, it can furthermore refine any existing line segments through an optimization


Paper:
https://arxiv.org/abs/2212.07766v1

Github:
https://github.com/cvg/deeplsd

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


@computer_science_and_programming

Accurate and Efficient Stereo Matching via Attention Concatenation Volume

Stereo Depth Estimation

Paper:
https://arxiv.org/pdf/2209.12699.pdf

Github:
https://github.com/gangweiX/Fast-ACVNet

Demo:
https://www.youtube.com/watch?v=az4Z3dp72Zw

ONNX:
ONNX-FastACVNet-Stereo-Depth-Estimation

@computer_science_and_programming

Happy New Year!

Summary of our channel for 2022.
(thanks for curated summary for TGSTAT team)

TGSTAT team: In the new 2023 year, we wish a rapid increase in subscribers, high posts reach, high-quality active audience and, of course, happiness and health.

A traditional present from us is a New Year card with your channel's this year results.

See you in 2023,

@computer_science_and_programming

PACO: Parts and Attributes of Common Objects

Sometimes object detection is not enough and you need more detail about object. Especially, when parts of objects is matters in your task. Then this dataset is for you from Facebook research team.

PACO is a detection dataset that goes beyond traditional object boxes and masks and provides richer annotations such as part masks and attributes. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets.

Paper:
https://arxiv.org/pdf/2301.01795.pdf

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

Visualization:
https://github.com/facebookresearch/paco/tree/main/notebooks

@computer_science_and_programming