Golnaz Ghiasi

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The presence of occluders significantly impacts performance of systems for object recognition. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and keypoint(More)
CNN architectures have terrific recognition performance but rely on spatial pooling which makes it difficult to adapt them to tasks that require dense, pixel-accurate labeling. This paper makes two contributions: (1) We demonstrate that while the apparent spatial resolution of convolutional feature maps is low, the high-dimensional feature representation(More)
The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and landmark localization that(More)
Occlusion poses a significant difficulty for object recognition due to the combinatorial diversity of possible occlusion patterns. We take a strongly supervised, non-parametric approach to modeling occlusion by learning deformable models with many local part mixture templates using large quantities of synthetically generated training data. This allows the(More)
This paper proposes, an efficient method for text independent writer identification using a codebook. The occurrence histogram of the shapes in the codebook is used to create a feature vector for the handwriting. There is a wide variety of different shapes in the connected components obtained from handwriting. Small fragments of connected components should(More)
In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the(More)
Standard databases provide for evaluation and comparison of various pattern recognition techniques by different researchers; thus they are essential for the advance of research. There are different handwritten databases in various languages, but there is not a large standard database of handwritten text for the evaluation of different algorithms for writer(More)