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


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.


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

- 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:

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.