In this work, we present a novel multi-scale feature fusion network (M-FFN) for image
captioning task to incorporate discriminative features and scene contextual information of an …

Existing image captioning approaches fail to generate fine-grained captions due to the lack
of rich encoding representation of an image. In this paper, we present an attentive contextual …

Disentangled representation learning finds compact, independent and easy-to-interpret
factors of the data. Learning such has been shown to require an inductive bias, which we …

Extrapolating fine-grained pixel-level correspondences in a fully unsupervised manner from
a large set of misaligned images can benefit several computer vision and graphics …

Introducing variation in the training dataset through data augmentation has been a popular
technique to make Convolutional Neural Networks (CNNs) spatially invariant but leads to …

Most learning methods for 3D data suffer significant performance drops when the data is not
carefully aligned to a canonical orientation. Aligning real world 3D data collected from …

Accurately and quickly obtaining information from garbage bins has great application value
in smart city construction and urban environmental management. However, existing deep …

Develo** computational algorithms to model the biological vision system has challenged
researchers in the computer vision field for several decades. As a result, state-of-the-art …