@inbook {bCampos, title = {Sentiment concept embedding for visual affect recognition}, booktitle = {Multimodal Behavior Analysis in theWild}, year = {2018}, publisher = {Elsevier}, organization = {Elsevier}, edition = {1}, chapter = {16}, abstract = {

Automatic sentiment and emotion understanding of general visual content has recently garnered much research attention. However, the large visual variance associated with\ high-level affective concepts presents a challenge when designing systems with high-performance requirements. One\ popular approach to bridge the {\textquotedblleft}affective gap{\textquotedblright} between\ low-level visual features and affective semantics consists of\ using Adjective Noun Pair (ANP) semantic constructs for\ concepts, e.g. {\textquotedblleft}beautiful landscape{\textquotedblright} or {\textquotedblleft}scary face{\textquotedblright} which\ act as a mid-level representation that can be recognized by\ visual classifers while still carrying an affective bias. In\ this work, we formulate the ANP detection task in images\ over a continuous space defined over an embedding that\ captures the inter-concept relationships between ANPs. We\ show how the compact representations obtained from the\ embeddeding extend the discrete concepts in the ontology\ and can be used for improved visual sentiment and emotion\ prediction, as well as new applications such as zero-shot\ ANP detection.

}, url = {https://www.elsevier.com/books/multimodal-behavior-analysis-in-the-wild/alameda-pineda/978-0-12-814601-9}, author = {V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto and Jou, Brendan and Jordi Torres and Chang, Shih-Fu} } @conference {cCampos18, title = {Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2018}, month = {01/2018}, abstract = {

Recurrent Neural Networks (RNNs) continue to show\  outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models.

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Chang, Shih-Fu} } @conference {cCampos, title = {Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks}, booktitle = {NIPS Time Series Workshop 2017}, year = {2017}, month = {08/2017}, address = {Long Beach, CA, USA}, abstract = {

Recurrent Neural Networks (RNNs) continue to show \ outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models.

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, url = {https://imatge-upc.github.io/skiprnn-2017-telecombcn/}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Chang, Shih-Fu} }