2 research outputs found
An Attention Model for group-level emotion recognition
In this paper we propose a new approach for classifying the global emotion of
images containing groups of people. To achieve this task, we consider two
different and complementary sources of information: i) a global representation
of the entire image (ii) a local representation where only faces are
considered. While the global representation of the image is learned with a
convolutional neural network (CNN), the local representation is obtained by
merging face features through an attention mechanism. The two representations
are first learned independently with two separate CNN branches and then fused
through concatenation in order to obtain the final group-emotion classifier.
For our submission to the EmotiW 2018 group-level emotion recognition
challenge, we combine several variations of the proposed model into an
ensemble, obtaining a final accuracy of 64.83% on the test set and ranking 4th
among all challenge participants.Comment: 5 pages, 3 figures, 2 table
GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions
Despite the remarkable success of generative adversarial networks, their
performance seems less impressive for diverse training sets, requiring learning
of discontinuous mapping functions. Though multi-mode prior or multi-generator
models have been proposed to alleviate this problem, such approaches may fail
depending on the empirically chosen initial mode components. In contrast to
such bottom-up approaches, we present GAN-Tree, which follows a hierarchical
divisive strategy to address such discontinuous multi-modal data. Devoid of any
assumption on the number of modes, GAN-Tree utilizes a novel mode-splitting
algorithm to effectively split the parent mode to semantically cohesive
children modes, facilitating unsupervised clustering. Further, it also enables
incremental addition of new data modes to an already trained GAN-Tree, by
updating only a single branch of the tree structure. As compared to prior
approaches, the proposed framework offers a higher degree of flexibility in
choosing a large variety of mutually exclusive and exhaustive tree nodes called
GAN-Set. Extensive experiments on synthetic and natural image datasets
including ImageNet demonstrate the superiority of GAN-Tree against the prior
state-of-the-arts.Comment: ICCV 2019 (code available at https://github.com/val-iisc/GANTree