J
Jiang Wang
Researcher at Baidu
Publications - 66
Citations - 13763
Jiang Wang is an academic researcher from Baidu. The author has contributed to research in topics: Convolutional neural network & Recurrent neural network. The author has an hindex of 33, co-authored 63 publications receiving 11600 citations. Previous affiliations of Jiang Wang include Microsoft & Fudan University.
Papers
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Proceedings ArticleDOI
Mining actionlet ensemble for action recognition with depth cameras
TL;DR: An actionlet ensemble model is learnt to represent each action and to capture the intra-class variance, and novel features that are suitable for depth data are proposed.
Proceedings Article
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
TL;DR: The m-RNN model directly models the probability distribution of generating a word given previous words and an image, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
Proceedings ArticleDOI
Attention to Scale: Scale-Aware Semantic Image Segmentation
TL;DR: Zhang et al. as discussed by the authors propose an attention mechanism that learns to softly weight the multi-scale features at each pixel location, which not only outperforms average and max-pooling, but also allows diagnostically visualize the importance of features at different positions and scales.
Posted Content
Learning Fine-grained Image Similarity with Deep Ranking
Jiang Wang,Yang Song,Thomas Leung,Charles J. Rosenberg,Jinbin Wang,James Philbin,Bo Chen,Ying Wu +7 more
TL;DR: A deep ranking model that employs deep learning techniques to learn similarity metric directly from images has higher learning capability than models based on hand-crafted features and deep classification models.
Proceedings ArticleDOI
Learning Fine-Grained Image Similarity with Deep Ranking
Jiang Wang,Yang Song,Thomas Leung,Charles J. Rosenberg,Jingbin Wang,James Philbin,Bo Chen,Ying Wu +7 more
TL;DR: Zhang et al. as mentioned in this paper proposed a deep ranking model that employs deep learning techniques to learn similarity metric directly from images, which has higher learning capability than models based on hand-crafted features.