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20172019
most citedMultimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

43 citations · 66 across the 4 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.LG201918 cited

Emotion Recognition with Spatial Attention and Temporal Softmax Pooling

Masih Aminbeidokhti, Marco Pedersoli, Patrick Cardinal +1

Video-based emotion recognition is a challenging task because it requires to distinguish the small deformations of the human face that represent emotions, while being invariant to…

cs.CV201943 cited

Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

Juan D. S. Ortega, Mohammed Senoussaoui, Eric Granger +3

This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition. The proposed DNN architecture has independe…

cs.CV2019

Min-max Entropy for Weakly Supervised Pointwise Localization

Soufiane Belharbi, Jérôme Rony, Jose Dolz +3

Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in me…

cs.CV20192 cited

Audio-Visual Kinship Verification

Xiaoting Wu, Eric Granger, Xiaoyi Feng

Visual kinship verification entails confirming whether or not two individuals in a given pair of images or videos share a hypothesized kin relation. As a generalized face verificat…

cs.LG2019

Variational Fair Clustering

Imtiaz Masud Ziko, Eric Granger, Jing Yuan +1

We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, includi…