Learning to Associate Words and Images Using a Large-scale Graph

We develop an approach for unsupervised learning of associations between co-occurring perceptual events using a large graph. We applied this approach to successfully solve the image captcha of China's railroad system. The approach is based on the principle of suspicious coincidence. In this particular problem, a user is presented with a deformed picture of a Chinese phrase and eight low-resolution images. They must quickly select the relevant images in order to purchase their train tickets. This problem presents several challenges: (1) the teaching labels for both the Chinese phrases and the images were not available for supervised learning, (2) no pre-trained deep convolutional neural networks are available for recognizing these Chinese phrases or the presented images, and (3) each captcha must be solved within a few seconds. We collected 2.6 million captchas, with 2.6 million deformed Chinese phrases and over 21 million images. From these data, we constructed an association graph, composed of over 6 million vertices, and linked these vertices based on co-occurrence information and feature similarity between pairs of images. We then trained a deep convolutional neural network to learn a projection of the Chinese phrases onto a 230-dimensional latent space. Using label propagation, we computed the likelihood of each of the eight images conditioned on the latent space projection of the deformed phrase for each captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on average. Our work, in answering this practical challenge, illustrates the power of this class of unsupervised association learning techniques, which may be related to the brain's general strategy for associating language stimuli with visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201

Unleashing the Potential of Philanthropy in China

Countries like China, but also Brazil, India, Indonesia and South Africa, are becoming more involved in development assistance not only through government aid but also through private investment, remittances and homegrown philanthropy. As the world looks for additional sources of funding to finance its fight against poverty, inequality and climate change, a lot of hope is resting on the rise of philanthropy. A strong and healthy philanthropic sector in China, confident in looking outside its borders and with the right capacities to respond to the great demands, will benefit China, as well as the rest of the world. This report believes that China today has the unprecedented opportunity to tap into its expanding non-profit and philanthropic sector. Home to record numbers of billionaires who have started to give back, with more and more corporations investing in corporate social responsibility (CSR) and with an expanding middle class increasingly aware of environmental and social challenges, China has vast resources to mobilize in support of philanthropy. In the last few years, technology and new media have created innovative ways to donate, which are making it even easier for the general public to participate in philanthropy. Finally, as Chinese businesses and state-owned companies continue to go global, China's philanthropists are also starting to look beyond their borders

Huge automatically extracted training sets for multilingual Word Sense Disambiguation

We release to the community six large-scale sense-annotated datasets in multiple language to pave the way for supervised multilingual Word Sense Disambiguation. Our datasets cover all the nouns in the English WordNet and their translations in other languages for a total of millions of sense-tagged sentences. Experiments prove that these corpora can be effectively used as training sets for supervised WSD systems, surpassing the state of the art for low- resourced languages and providing competitive results for English, where manually annotated training sets are accessible. The data is available at trainomatic. org