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EDLCV 2020 : Joint Workshop on Efficient Deep Learning in Computer Vision

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Link: https://workshop-edlcv.github.io
 
When Jun 15, 2020 - Jun 15, 2020
Where Seattle, WA, USA
Submission Deadline Mar 25, 2020
Notification Due Apr 12, 2020
Final Version Due Apr 19, 2020
Categories    computer vision   deep learning   efficient machine learning   neural networks
 

Call For Papers

Computer Vision has a long history of academic research, and recent advances in deep learning have provided significant improvements in the ability to understand visual content. As a result of these research advances on problems such as object classification, object detection, and image segmentation, there has been a rapid increase in the adoption of Computer Vision in industry; however, mainstream Computer Vision research has given little consideration to speed or computation time, and even less to constraints such as power/energy, memory footprint and model size.

This workshop, co-located with CVPR 2020, addresses the following topics:

Efficient Neural Network and Architecture Search
- Compact and efficient neural network architecture for mobile and AR/VR devices
- Hardware (latency, energy) aware neural network architectures search, targeted for mobile and AR/VR devices
- Efficient architecture search algorithm for different vision tasks (detection, segmentation etc.)
- Optimization for Latency, Accuracy and Memory usage, as motivated by embedded devices

Neural Network Compression
- Model compression (sparsification, binarization, quantization, pruning, thresholding and coding etc.) for efficient inference with deep networks and other ML models
- Scalable compression techniques that can cope with large amounts of data and/or large neural networks (e.g., not requiring access to complete datasets for hyperparameter tuning and/or retraining)
- Hashing (Binary) Codes Learning

Low-bit Quantization Network and Hardware Accelerators
- Investigations into the processor architectures (CPU vs GPU vs DSP) that best support mobile applications
- Hardware accelerators to support Computer Vision on mobile and AR/VR platforms
- Low-precision training/inference & acceleration of deep neural networks on mobile devices

Dataset and benchmark
- Open datasets and test environments for benchmarking inference with efficient DNN representations
- Metrics for evaluating the performance of efficient DNN representations
- Methods for comparing efficient DNN inference across platforms and tasks

Label/sample/feature efficient learning
- Label Efficient Feature Representation Learning Methods, e.g. Unsupervised Learning, Domain Adaptation, Weakly Supervised Learning and SelfSupervised Learning Approaches
- Sample Efficient Feature Learning Methods, e.g. Meta Learning
- Low Shot learning Techniques
- New Applications, e.g. Medical Domain

Mobile and AR/VR Applications
- Novel mobile and AR/VR applications using Computer Vision such as image processing (e.g. style transfer, body tracking, face tracking) and augmented reality
- Learning efficient deep neural networks under memory and computation constraints for on-device applications

All submissions will be handled electronically via the workshop’s CMT Website. Click the following link to go to the submission site: https://cmt3.research.microsoft.com/EDLCV2020/

Papers should describe original and unpublished work about the related topics. Each paper will receive double blind reviews, moderated by the workshop chairs. Authors should take into account the following:

- All papers must be written and presented in English.
- All papers must be submitted in PDF format. The workshop paper format guidelines are the same as the Main Conference papers
- The maximum paper length is 8 pages (excluding references). Note that shorter submissions are also welcome.
- The accepted papers will be published in CVF open access as well as in IEEE Xplore.

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