Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition
By Artsiom Ablavatski, Shijian Lu, Jianfei Cai. 2017
39 citations recorded by the source corpus.
We design an Enriched Deep Recurrent Visual Attention Model (EDRAM) — an improved attention-based architecture for multiple object recognition. The proposed model is a fully differentiable unit that can be optimized end-to-end by using Stochastic Gradient Descent (SGD). The Spatial Transformer (ST) was employed as visual attention mechanism which allows to learn the geometric transformation of objects within images. With the combination of the Spatial Transformer and the powerful recurrent architecture, the proposed EDRAM can localize and recognize objects simultaneously. EDRAM has been evaluated on two publicly available datasets including MNIST Cluttered (with 70K cluttered digits) and SVHN (with up to 250k real world images of house numbers). Experiments show that it obtains superior performance as compared with the state-of-the-art models.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Jitendra Malik
- Dacheng Tao
- Chunhua Shen
- Trevor Darrell
- Andrea Vedaldi
- Xiaogang Wang
Citations in the corpus
- Towards Interpretable Reinforcement Learning Using Attention Augmented Agents (2019, background): “image classification and captioning [12, 13, 14, 15, 16, 17, 18, 19, 20], text classification [21, 22], generative models [6, 23, 24], object tracking [25], and reinforcement learning [26, 27, 28].”
- Detecting Curve Text in the Wild: New Dataset and New Solution (2017, methodology): “The recent popular recognition methods [29, 28, 1, 19] all require single row text line in each bounding box.”
- Towards Robust Image Classification Using Sequential Attention Models (2019, background): “…such as question-answering [24], machine translation [6, 52], video classification and captioning [46, 33], image classification and captioning [37, 11, 17, 1, 55, 60, 53, 5, 57], text classification [58, 47], generative models [42, 59, 30], object tracking [29], and reinforcement learning [10].”