2020 Fiscal Year Research-status Report
Zero-shot recognition of generic objects
| Project/Area Number |
19K24344
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| Research Institution | Kobe University |
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Principal Investigator |
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| Project Period (FY) |
2019-08-30 – 2022-03-31
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| Keywords | Zero-Shot Learning / Self-Supervised Learning / Visual Representation |
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| Outline of Annual Research Achievements |
In this academic year of research, efforts have been focused along two axis.
Along the first axis, self-supervised visual representations of the Generic Object ZSL (GOZ) Dataset images were proposed and compared with traditional supervised representations on the GOZ benchmark. These representations tend to perform better on standard zero-shot learning task whereas they do not match the supervised representations on the generalized zero-shot learning setting. Closing the gap between closely clustered supervised representations that perform well on training classes and more scattered unsupervised representations on the training classes while retaining higher accuracy on the unseen test classes has been identified as a promising research question.
The second axis concerns the computational efficiency of Convolutional Neural Network (CNN) training. Indeed, training CNN on Imagenet scale dataset is computationally very expensive, which hinders the investigation of different training and fine-tuning strategies. Towards that end, we have focused our efforts on reducing both the amount of computations and the memory footprint of CNN training in order to enable larger batch training, and hence shorter training times.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
Zero-Shot Learning and Self-Supervised Learning of visual representations are two topics at the frontier of computer vision research. Catching up with recent research on self-supervision while evaluating their efficiency on Zero-Shot Learning has been a very time consuming task.
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| Strategy for Future Research Activity |
In our final year of study we plan to publish the results of our investigation, including a publication on GPU memory optimization and self-supervised visual representation learning.
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| Causes of Carryover |
Most of the expenses incurred have been allocated to electronic supply necessary for the laboratory’s daily research work. Additional fees were incurred to attend conferences and purchase scientific articles.
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