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20142023
most citedDeep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

652 citations · 1.7k across the 25 of their papers we have counts for

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Showing 2014Show all

11 papers · 1 filter

cs.CV2014

Semantic Part Segmentation using Compositional Model combining Shape and Appearance

Jianyu Wang, Alan Yuille

In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation task…

cs.CV20141 cited

Representing Data by a Mixture of Activated Simplices

Chunyu Wang, John Flynn, Yizhou Wang +1

We present a new model which represents data as a mixture of simplices. Simplices are geometric structures that generalize triangles. We give a simple geometric understanding that…

cs.CV2014652 cited

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

Junhua Mao, Wei Xu, Yi Yang +3

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a w…

cs.CV2014370 cited

Explain Images with Multimodal Recurrent Neural Networks

Junhua Mao, Wei Xu, Yi Yang +2

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the pr…

cs.CV2014329 cited

Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations

Xianjie Chen, Alan Yuille

We present a method for estimating articulated human pose from a single static image based on a graphical model with novel pairwise relations that make adaptive use of local image…

cs.LG2014115 cited

Learning Deep Structured Models

Liang-Chieh Chen, Alexander G. Schwing, Alan L. Yuille +1

Many problems in real-world applications involve predicting several random variables which are statistically related. Markov random fields (MRFs) are a great mathematical tool to e…