Last night we had the 7th Paris Machine Learning Meetup at DojoEvents. We first had two Skype presentations of crowdfunded projects that are connected to Machine Vision. The Q&As are in English and before watching the video above you want to watch the VMX video introduction which is what the attendees saw before the Hangout started. It is here.
- Tomasz Malisiewicz, VMX Project: Computer Vision for Everyone (English)
- Allen Yang, Atheer One, what it feels like to have superpowers (English)
- Patrick Perez From image to descriptors and back again (French)
- Kenji Lefèvre-Hasegawa , 'Dataiku Science Studio', What does it take to win the Kaggle/Yandex competition ? (French)
Abstracts:
From image to descriptors and back again , Patrick Perez
The context of this presentation is visual search, with a specific focus on retrieving images similar to a query image. I will first discuss one corner stone of such large-scale systems: aggregation of local descriptors (typically SIFTs) into a fixed-sized image signature. I shall present "Vector of Locally Aggregated Descriptors" (VLAD) which offers a powerful alternative to popular "Bag-of-Word" (BoF) approach. Combined with an efficient indexing system, VLAD can lead to a memory footprint as compact as 16 bytes per image with good approximate search performance. I will then touch upon risks of visual information leakage in such image search systems, showing that human-understandable reconstruction of an image can be obtained from the sparse set of its local descriptors, and no other side information.
What does it take to win the Kaggle/Yandex competition, Kenji Lefèvre-Hasegawa
I'll give a feedback on the "personnalized web search" Kaggle challenge for which our team won First prize. I will focus the talk on both the experience itself (team work and tools) and the models we've used.
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