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Recently, the capability of character-level evaluation measures for machine translation output has been confirmed by several metrics. This work proposes translation edit rate on character level (CharacTER), which calculates the character level edit distance while performing the shift edit on word level. The novel metric shows high system-level correlation(More)
This paper addresses the problem of detecting coherent motions in crowd scenes and subsequently constructing semantic regions for activity recognition. We first introduce a coarse-to-fine thermal-diffusion-based approach. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion filed, named as thermal energy field. The(More)
Spatial contextual dependencies are crucial for scene labeling problems. Recurrent neural network (RNN) is one of state-of-the-art methods for modeling contextual dependencies. However, RNNs are fundamentally designed for sequential data, not spatial data. This work shows that directly applying traditional RNN architectures, which unfold a 2D lattice grid(More)
This paper addresses the problem of detecting coherent motions in crowd scenes and presents its two applications in crowd scene understanding: semantic region detection and recurrent activity mining. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion field named thermal energy field. The thermal energy field is able(More)
Recently, neural network models have achieved consistent improvements in statistical machine translation. However, most networks only use one-hot encoded input vectors of words as their input. In this work, we investigated the exponentially decaying bag-of-words input features for feed-forward neural network translation models and proposed to train the(More)
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