WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query.

 

Classes in this dataset:

Airplane

Baseball Diamond

Buildings

Freeway

Golf Course

Harbor

Intersection

Mobile home park

Overpass

Parking lot

River

Runway

Storage tank

Tennis court

Paper

The paper is also available on ArXiv: A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

 

Feel free to cite the author, if the work is any help to you:

 

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

 

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With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query..

Classes in this dataset:

  1. Airplane
  2. Baseball Diamond
  3. Buildings
  4. Freeway
  5. Golf Course
  6. Harbor
  7. Intersection
  8. Mobile home park
  9. Overpass
  10. Parking lot
  11. River
  12. Runway
  13. Storage tank
  14. Tennis court

Paper

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

Categories:
40 Views

With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query..

Classes in this dataset:

  1. Airplane
  2. Baseball Diamond
  3. Buildings
  4. Freeway
  5. Golf Course
  6. Harbor
  7. Intersection
  8. Mobile home park
  9. Overpass
  10. Parking lot
  11. River
  12. Runway
  13. Storage tank
  14. Tennis court

Paper

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

Categories:
27 Views

Semantic segmentation is the topic of interest among deep learning researchers in the recent era.  It has many applications in different domains including, food recognition. In the case of food recognition, it removes the non-food background from the food portion. There is no large public food dataset available to train semantic segmentation models. We prepared a dataset named ’SEG-FOOD’[44] containing images of FOOD101, PFID, and Pakistani Food dataset and open-sourced the annotated dataset for future research. We annotated the images using JS Segment annotator.

Instructions: 

*  For detailed experimentation, please refer to our paper which is under review. we will update the link of that later.

* For starter code please refer to our Github repository. https://github.com/ghalib2021/SEGFOOD

* Note: This dataset contains images from Food101, PFID, and Pakistani Food Dataset. Our main contribution is the manual annotation of the food images for background removal using semantic segmentation and collection of Pakistani food dataset images. Please cite our work besides the original dataset collector if you are using a segmented dataset otherwise, cite the original dataset collector.

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