Learning in Data Scarce Visual and Multimodal Applications Using Vectorized
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Microsoft Research334 тыс
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249 дней – 57946:03Опубликовано 12 апреля 2019, 16:26
Learning in Data Scarce Visual and Multimodal Applications Using Vectorized and Composable Representations
The vision and learning group at CVT (Center for Vision Technologies) at SRI has developed a framework and a suite of algorithms for machine learning in data scarce conditions. We present our results on zero shot object detection as well as multi way retrieval for social media applications using multimodal embeddings. We will then present a novel spatiotemporal graph convolutional network that enables composable representations. We show results with activity detection.
See more at microsoft.com/en-us/research/v...
The vision and learning group at CVT (Center for Vision Technologies) at SRI has developed a framework and a suite of algorithms for machine learning in data scarce conditions. We present our results on zero shot object detection as well as multi way retrieval for social media applications using multimodal embeddings. We will then present a novel spatiotemporal graph convolutional network that enables composable representations. We show results with activity detection.
See more at microsoft.com/en-us/research/v...
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