Now showing items 1-6 of 6

    • Diving deep into sentiment: understanding fine-tuned CNNs for visual sentiment prediction 

      Campos Camúñez, Víctor; Salvador Aguilera, Amaia; Jou, Brendan; Giró Nieto, Xavier (Association for Computing Machinery (ACM), 2015)
      Conference lecture
      Open Access
      Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural ...
    • From pixels to sentiment: fine-tuning CNNs for visual sentiment prediction 

      Campos Camunez, Victor; Jou, Brendan; Giró Nieto, Xavier (2017-02-05)
      Article
      Restricted access - publisher's policy
      Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich ...
    • Is a “happy dog” more “happy” than “dog”? - Adjective and Noun Contributions for Adjective-Noun Pair prediction 

      Fernàndez, Dèlia; Campos Camúñez, Victor; Jou, Brendan; Giró Nieto, Xavier; Chang, Shih-Fu (2016)
      Conference lecture
      Open Access
    • More cat than cute?: interpretable prediction of adjective-noun pairs 

      Fernàndez, Dèlia; Woodward, Alejandro; Campos Camunez, Victor; Giró Nieto, Xavier; Jou, Brendan; Chang, Shih-Fu (2017)
      Conference report
      Restricted access - publisher's policy
      The increasing availability of affect-rich multimedia resources has bolstered interest in understanding sentiment and emotions in and from visual content. Adjective-noun pairs (ANP) are a popular midlevel semantic ...
    • Skip RNN: learning to skip state updates in recurrent neural networks 

      Campos, Víctor; Jou, Brendan; Giró Nieto, Xavier; Torres Viñals, Jordi; Chang, Shih-Fu (Barcelona Supercomputing Center, 2018-04-24)
      Conference report
      Open Access
      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 ...
    • Skip RNN: learning to skip state updates in recurrent neural networks 

      Campos Camunez, Victor; Jou, Brendan; Giró Nieto, Xavier; Torres Viñals, Jordi; Chang, Shih-Fu (2018)
      Conference lecture
      Open Access
      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 ...