Papers by Larry Davis
WSLLN:Weakly Supervised Natural Language Localization Networks (D19-1)
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| Challenge: | Existing methods to learn correspondence between visual segments and texts require temporal coordinates for training, which leads to high costs of annotation. |
| Approach: | They propose weakly supervised language localization networks to detect events in untrimmed videos . they train with only video-sentence pairs without accessing to temporal locations of events . |
| Outcome: | Experiments on ActivityNet Captions and DiDeMo show that WSLLN performs state-of-the-art. |
Learning to Color from Language (N18-2)
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| Challenge: | Automatic colorization is the process of adding color to greyscale images. |
| Approach: | They propose two different architectures for language-conditioned colorization that produce more accurate and plausible colorizations than a language-agnostic version. |
| Outcome: | The proposed architectures produce more accurate and plausible colorizations than a language-agnostic version. |