Special Report, Entertainment, Americas

[ms-marvelesold]

To our best knowledge, the concept of exploiting temporal structure, e.g., for video captioning, for generating fine-grained feature set with bilinear pooling, is yet unexplored. Another research track focuses on visual feature sophistication with an intuition that richer features can lead to subtle clues for guiding the decoder. A straightforward approach is to capture features from different architectures and unify them into a fixed dimension. The networks can be frame-level 2D CNN. Clip-level 3D CNN features. We argue that word prediction models can better differentiate between the candidate vocabularies when features from multiple origins are fed into the decoder. Also, the aggregation approach of the works above fuse features at a single stage, either by concatenation or with a linear layer. Yet, hierarchical dependence between the streams was ignored. In this scenario, a feature stream can hardly identify attributes already addressed by the prior streams, resulting in redundancy. This work proposed a module that attends to specific feature sets from respective streams in a forward pass.




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[wangiskahn2]

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