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

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. The workshop has three main goals on solving and discussing efficiency in Computer Vision: 

First, the workshop aims to create a venue for a consideration of the new generation of problems that arise as Computer Vision meets mobile and AR/VR systems constraints, to bring together researchers, educators and practitioners who are interested in techniques as well as applications of compact, efficient neural network representations. The workshop discussions will establish close connection between researchers in machine learning and computer vision communities and engineers in industry, and to benefit both academic researchers as well as industrial practitioners. 

Second, the workshop aims at reproducibility and comparability of methods for compact and efficient neural network representations, and on-device machine learning. Thus a set of benchmarking tasks (image classification, visual question answering) will be provided together with defined data sets, in order to compare the performance of neural network compression methods on the same networks. Submissions are encouraged (but not required) to use these tasks and data sets in their work. Also, contributors are encouraged to make their code available. 

Third, the workshop aims to discuss the next steps in developing efficient feature representations from three aspects: energy efficient, label efficient, and sample efficient. Despite DNNs are brain-inspired and can achieve or even surpass human-level performance on a variety of challenging computer vision tasks, they continue to trail humans’ abilities in many aspects, such as high energy-efficiency and the ability to perform low-shot learning (learning novel concepts from very few examples). Therefore, the next generation of feature representation and learning techniques should aim to tackle recognition tasks with significantly reduced computational complexity, using as little training data as people need, and to generalize to a range of different tasks beyond the one task the model was trained on.

EDLCV 2020 : Joint Workshop on Efficient Deep Learning in Computer Vision will take place in Seattle, WA, USA. It’s a 1 day event starting on Jun 15, 2020 (Monday) and will be winded up on Jun 15, 2020 (Monday).

EDLCV 2020 falls under the following areas: COMPUTER VISION, DEEP LEARNING, MACHINE LEARNING, NEURAL NETWORK, etc. Submissions for this Workshop can be made by Mar 25, 2020. Authors can expect the result of submission by Apr 12, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Apr 19, 2020 to the official website of the Workshop.

Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines.

Other Details of the EDLCV 2020

• Short Name: EDLCV 2020
• Full Name: Joint Workshop on Efficient Deep Learning in Computer Vision
• Timing: 09:00 AM-06:00 PM (expected)
• Fees: Check the official website of EDLCV 2020
• Event Type: Workshop
• Website Link: https://workshop-edlcv.github.io/
• Location/Address: Seattle, WA, USA