Modeling Emotion Influence from Images in Social Networks

Images become an important and prevalent way to express users' activities, opinions and emotions. In a social network, individual emotions may be influenced by others, in particular by close friends. We focus on understanding how users embed emotions into the images they uploaded to the social websites and how social influence plays a role in changing users' emotions. We first verify the existence of emotion influence in the image networks, and then propose a probabilistic factor graph based emotion influence model to answer the questions of "who influences whom". Employing a real network from Flickr as experimental data, we study the effectiveness of factors in the proposed model with in-depth data analysis. Our experiments also show that our model, by incorporating the emotion influence, can significantly improve the accuracy (+5%) for predicting emotions from images. Finally, a case study is used as the anecdotal evidence to further demonstrate the effectiveness of the proposed model

Interpretable aesthetic features for affective image classification

Images can not only display contents themselves, but also convey emotions, e.g., excitement, sadness. Affective image classification is useful and hot in many fields such as comput-er vision and multimedia. Current researches usually consid-er the relationship model between images and emotions as a black box. They extract the traditional discursive visual fea-tures such as SIFT and wavelet textures, and use them di-rectly upon various classification algorithms. However, these visual features are not interpretable, and people cannot know why such a set of features induce a particular emotion. And due to the highly subjective nature of images, the classifica-tion accuracies on these visual features are not satisfactory for a long time. We propose the interpretable aesthetic fea-tures to describe images inspired by art theories, which are intuitive, discriminative and easily understandable. Affective image classification based on these features can achieve high-er accuracy, compared with the state-of-the-art. Specifically, the features can also intuitively explain why an image tends to convey a certain emotion. We also develop an emotion guided image gallery to demonstrate the proposed feature collection. Index Terms — image features, affective classification, interpretability, art theory 1

FPGA-based systolic deconvolution architecture for upsampling

A deconvolution accelerator is proposed to upsample n × n input to 2n × 2n output by convolving with a k × k kernel. Its architecture avoids the need for insertion and padding of zeros and thus eliminates the redundant computations to achieve high resource efficiency with reduced number of multipliers and adders. The architecture is systolic and governed by a reference clock, enabling the sequential placement of the module to represent a pipelined decoder framework. The proposed accelerator is implemented on a Xilinx XC7Z020 platform, and achieves a performance of 3.641 giga operations per second (GOPS) with resource efficiency of 0.135 GOPS/DSP for upsampling 32 × 32 input to 256 × 256 output using a 3 × 3 kernel at 200 MHz. Furthermore, its high peak signal to noise ratio of almost 80 dB illustrates that the upsampled outputs of the bit truncated accelerator are comparable to IEEE double precision results

A deep recurrent approach for acoustic-to-articulatory inversion

To solve the acoustic-to-articulatory inversion problem, this paper proposes a deep bidirectional long short term memory recurrent neu-ral network and a deep recurrent mixture density network. The artic-ulatory parameters of the current frame may have correlations with the acoustic features many frames before or after. The traditional pre-designed fixed-length context window may be either insufficient or redundant to cover such correlation information. The advantage of recurrent neural network is that it can learn proper context infor-mation on its own without the requirement of externally specifying a context window. Experimental results indicate that recurrent model can produce more accurate predictions for acoustic-to-articulatory inversion than deep neural network having fixed-length context win-dow. Furthermore, the predicted articulatory trajectory curve of re-current neural network is smooth. Average root mean square error of 0.816 mm on the MNGU0 test set is achieved without any post-filtering, which is state-of-the-art inversion accuracy. Index Terms — long short term memory (LSTM), recurrent nueral network (RNN), mixture density network (MDN), layer-wise pre-training 1