Efficient Deep Learning in Computer Vision (EDLCV) Call for Papers

Joint Workshop on Efficient Deep Learning in Computer Vision (EDLCV) 

			June 15, 2020
		at CVPR 2020, Seattle, WA, USA

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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.

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

Important Dates

Paper Submission Deadline: March 25, 2020 pst
Notification to authors: April 12, 2020 pst
Camera ready deadline: April 19, 2020 pst
Workshop: June 15, 2020 (Full Day)

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.